Methods of calibrating an imaging system using calibration beads

ABSTRACT

When utilized in a flow imaging instrument, calibration beads provide a known data source that can be employed in various self-diagnostic, calibration and quality metric applications for the both the optical system of the flow imaging instrument, as well as the flow cell of the flow imaging instrument. Such data can be used to determine point spread functions associated with an imaging system, to determine a sensitivity of an imaging system, and to determine a focal point of the imaging system. Imagery collected from calibration beads can be used to determine core size and stability and TDI/flow speed synchronization. Calibration beads can be beneficially employed to enable stable system operation, even when very low sample concentration, or very small sample sizes are to be analyzed.

RELATED APPLICATIONS

This application is a continuation in part of a prior application Ser.No. 09/939,292, filed on Aug. 24, 2001, now U.S. Pat. No. 6,532,061which itself is based on a prior provisional application Serial No.60/228,076, filed on Aug. 25, 2000 now abandoned, the benefits of thefiling dates of which are hereby claimed under 35 U.S.C. § 119(e) and 35U.S.C. § 120.

FIELD OF THE INVENTION

The present invention generally relates to a method for usingcalibration beads to enhance the performance of a flow imaging system,and more specifically, to using calibration beads to facilitate thereliable collection of velocity data used by the flow imaging system.

BACKGROUND OF THE INVENTION

Cells and cell groupings are three-dimensional objects containing richspatial information. The distribution of a tremendous variety ofbio-molecules can be identified within a cell using an ever-increasingnumber of probes. In the post-genome era, there is mounting interest inunderstanding the cell, not only as a static structure, but as a dynamiccombination of numerous interacting feedback control systems. Thisunderstanding can lead to new drugs, better diagnostics, more effectivetherapies, and better health care management strategies. However, thisunderstanding will require the ability to extract a far greater amountof information from cells than is currently possible.

The principal technologies for cellular analysis are automatedmicroscopy and flow cytometry. The information generated by these maturetechnologies, although useful, is often not as detailed as desired.Automated microscopy allows two-dimensional (2D) imaging of from one tothree colors of cells on slides. Typical video frame rates limit kineticstudies to time intervals of 30 ms.

Instruments known as flow cytometers currently provide vital informationfor clinical medicine and biomedical research by performing opticalmeasurements on cells in liquid suspension. Whole blood, fractionatedcomponents of blood, suspensions of cells from biopsy specimens and fromcell cultures, and suspensions of proteins and nucleic acid chains aresome of the candidates suitable for analysis by flow cytometry. In flowcytometers specialized for routine blood sample analysis, cell typeclassification is performed by measuring the angular distribution oflight scattered by the cells and the absorption of light by speciallytreated and stained cells. The approximate numbers of red blood cells,white blood cells of several types, and platelets are reported as thedifferential blood count. Some blood-related disorders can be detectedas shifts in optical characteristics, as compared to baseline opticalcharacteristics, such shifts being indicative of morphological andhistochemical cell abnormalities. Flow cytometers have been adapted foruse with fluorescent antibody probes, which attach themselves tospecific protein targets, and for use with fluorescent nucleic acidprobes, which bind to specific DNA and RNA base sequences byhybridization. Such probes find application in medicine for thedetection and categorization of leukemia, for example, in biomedicalresearch, and drug discovery. By employing such prior art techniques,flow cytometry can measure four to ten colors from living cells.However, such prior art flow cytometry offers little spatial resolution,and no ability to study a cell over time. There is clearly a motivationto address the limitations of existing cell analysis technologies with anovel platform for high speed, high sensitivity cell imaging.

A key issue that arises in cell analysis carried out with imagingsystems is the measurement of the velocity of a cell or other objectthrough the imaging system. In a conventional time-domain methodology,cell velocity is measured using time-of-flight (TOF). Two detectors arespaced a known distance apart and a clock measures the time it takes acell to traverse the two detectors. The accuracy of a TOF measurement isenhanced by increasing detector spacing. However, this increases thelikelihood that multiple cells will occupy the measurement region,requiring multiple timers to simultaneously track all cells in view.Initially, the region between the detectors is cleared before startingsample flow. As cells enter the measurement region, each entry signal istimed separately. The system is synchronized with the sample by notingthe number of entry signals that occur before the first exit signal.

TOF velocity measurement systems are prone to desynchronization when theentry and exit signals are near threshold, noise is present, or expectedwaveform characteristics change due to the presence of different celltypes and orientations. Desynchronization causes errors in velocitymeasurement that can lead to degraded signals and misdiagnosed cellsuntil the desynchronized condition is detected and corrected.Resynchronization may require that all cells be cleared from the regionbetween the detectors before restarting sample flow, causing the loss ofsample.

Significant advancements in the art of flow cytometry are described incommonly assigned U.S. Pat. No. 6,249,341, issued on Jun. 19, 2001, andentitled IMAGING AND ANALYZING PARAMETERS OF SMALL MOVING OBJECTS SUCHAS CELLS, as well as in commonly assigned U.S. Pat. No. 6,211,955,issued on Apr. 3, 2001, also entitled IMAGING AND ANALYZING PARAMETERSOF SMALL MOVING OBJECTS SUCH AS CELLS. The specifications and drawingsof each of these patents are hereby specifically incorporated herein byreference.

The inventions disclosed in the above noted patents perform highresolution, high-sensitivity two-dimensional (2D) and three-dimensional(3D) imaging using time-delay-integration (TDI) electronic imageacquisition with cells in flow. These instruments are designed to expandthe analysis of biological specimens in fluid suspensions beyond thelimits of conventional flow cytometers. TDI sensors utilize solid-statephoton detectors such as charge-coupled device (CCD) arrays and shiftlines of photon-induced charge in synchronization with the flow of thespecimen. The method allows a long exposure time to increase asignal-to-noise ratio (SNR) in the image while avoiding blurring.However, precise synchronization of the TDI detector timing with themotion of the moving targets is required. For example, if a target is totraverse 100 lines of a TDI sensor to build an image, and the blurringis expected to be less than a single line width, then the velocity ofthe target must be known to less than one percent of its actual value.It would thus be desirable to provide method and apparatus capable ofproducing highly accurate flow velocity for such moving targets.

Several methods for determining velocity for use in such flow imaginginstruments are described in commonly assigned, copending applicationentitled MEASURING THE VELOCITY OF SMALL MOVING OBJECTS SUCH AS CELLS,Ser. No. 09/939,292, filed on Aug. 24, 2001, the specification anddrawings of which are hereby specifically incorporated by reference.

Proper functioning of flow imaging systems that require thesynchronization of a TDI detector with objects in flow requiresconsistent and reliable velocity information. This can be particularlydifficult to achieve when the fluid flow contains only a small number ofparticles, when only a small volume of sample fluid is available, whenonly limited amounts of light from target cells are available, and whena distribution of target cells in a sample is uneven. It would bedesirable to provide a method for determining reliable velocity dataunder such conditions. It would further be desirable to provide methodsto facilitate diagnostic and calibration procedures for flow imagingsystems.

SUMMARY OF THE INVENTION

The present invention is a method for utilizing calibration beads toenhance the performance of a flow imaging system. Related applicationshave described preferred flow imaging systems and preferred methods ofmeasuring the velocity of objects in flow passing through such systems.Such imaging systems are beneficially employed to produce images ofobjects of interest, such as biological cells.

Non sample particles, referred to as calibration beads, can beintroduced into a flow of fluid in such a flow imaging system for thepurpose of establishing a velocity. Such calibration beads arepreferably polymer micro spheres, but it should be understood thatsubstantially any object capable of being suspended in a fluid, andwhose dimensions are compatible with the imaging system being employed,can be utilized as a calibration bead. With respect to the dimensions,such calibration beads must be small enough to pass through the flowcell of the imaging system without obstruction, and yet large enough tobe readily detectable by the imaging systems optics and sensors. Anoptimal size calibration bead for a first imaging system may notrepresent an optimal size for a second imaging system. Examples ofparticles that can be beneficially employed as calibration beads includecells, cell clusters, labeled and unlabelled micro spheres (polymer,copolymer, tetra polymer and silica beads).

The use of such calibration beads is particularly helpful when the fluidflow contains only a small number of particles, when only a small volumeof sample fluid is available, when only limited amounts of light fromtarget cells are available, when a distribution of target cells in asample is uneven, and to facilitate diagnostic and calibrationprocedures.

When the fluid flow contains only a small number of particles it isuseful to employ a relatively high concentration of calibration beads,to enable the continuous detection of flow speed velocity. Thecontinuous velocity measurement enables continuous TDI detector/flowspeed synchronization, enhancing the stability and performance of flowimaging systems. When a sample particle is imaged, the stable TDIdetector/flow speed synchronization facilitates the collection of moreprecise sample data than can be achieved in an imaging system with poorTDI detector/flow speed synchronization. A preferred concentration ofcalibration beads will be selected based on parameters of the flowimaging system being employed, to ensure that sufficient calibrationbeads are provided so that the velocity of the fluid in the flow cell iscontinually monitored.

Another circumstance in which the use of calibration beads canfacilitate accurate velocity measurements is when the actual volume ofthe sample is small. A fluid flow of calibration beads (i.e. no sample)can be employed to initialize a flow imaging system, and to establish astable hydrodynamically focused fluid flow in the flow cell of such animaging system. The calibration beads enable the TDI Camera/Velocitysynchronization to be established. When the imaging system and fluidflow is stable, the sample containing the objects of imaging interestcan be introduced into the flow cell for analysis of the sample objects.

The amount of light from an object corresponds to the precision of thevelocity measurement. Lower levels of light provide less precisevelocity data. Often objects of interest (i.e.) samples are so smallthat while they do provide sufficient light to generate an image, thevelocity data obtainable is less precise than desired, thereby makingaccurate TDI synchronization with the fluid flow difficult. As thesignal strength of the light from the objects is generally proportionalthe size of the object, calibration beads that are larger than theanticipated size of the objects of interests can be employed to increasevelocity detection resolution.

In a related problem, samples can include several different types ofobjects of interest, each of which are of different sizes or properties,and each of which provide different levels of light from which imagesand velocity data can be obtained. Different levels of light correspondto different levels of precision in determining velocity, which in turnmeans that the TDI synchronization with the fluid flow can undesirablyvary. Adding calibration beads to such a sample volume provides aconsistent velocity signal, enabling TDI synchronization to be morereliably maintained.

When utilized in a flow imaging instrument, calibration beads can alsoprovides a known data source that can be employed in variousself-diagnostic, calibration and quality metric applications for theboth the optical system of the flow imaging instrument, as well as theflow cell of the flow imaging instrument. Such data can be used todetermine point spread functions associated with an imaging system, todetermine a sensitivity of an imaging system, and to determine a focalpoint of the imaging system. Imagery collected from calibration beadscan be used to determine core size and stability and TDI/flow speedsynchronization.

Preferred calibration beads are polymeric beads, including but notlimited to following types: polystyrene, styrene/divinylbenzenecopolymer (S/DVB), polymethylmethacrylate (PMMA), polyvinyltoluene(PVT), styrene/butadiene (S/B), styrene/vinyltoluene (S/VT). Mixtures ofdifferent types of calibration beads may be used. Preferable calibrationbeads will have densities in the range of 0.9-2.3 grams per cubiccentimeter, and diameters that range from 20 nanometers to 50 microns.Calibration beads may incorporate surface functional groups enabling thecovalent coupling of ligands. Such surface functional groups preferablyinclude: sulfate based groups (—SO₄), aldehyde based groups (—CHO),aliphatic amine based groups (—CH₂—NH₂), amide based groups (—CONH₂),aromatic amine based groups (—NH₂), carboxylic acid based groups(—COOH), chloromethyl based groups (—CH₂—Cl), hydrazide based groups(CONH—NH₂), hydroxyl based groups (—OH), and sulfonate based groups(—SO₃). Calibration beads can be beneficially incorporate a coating ofprotein A or streptavidin. Further, calibration beads can include dyedmicrospheres of different colors, fluorescent labeled microspheres,magnetic microspheres, and molecularly imprinted micro spheres.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The foregoing aspects and many of the attendant advantages of thisinvention will become more readily appreciated as the same becomesbetter understood by reference to the following detailed description,when taken in conjunction with the accompanying drawings, wherein:

FIG. 1 is a schematic diagram of a system for measuring the velocity ofobjects in a flow stream by detecting light scattered by the objects;

FIG. 2 is a schematic diagram of a system for measuring the velocity ofobjects in a flow stream by detecting light emitted by fluorescence bythe objects;

FIG. 3 is a schematic diagram of a system for measuring the velocity ofobjects in a flow stream by detecting the absorption of light by theobjects;

FIG. 4 is a schematic illustration of the concept of building a signalfrom the passage of the image of a bright object across a grating;

FIG. 5 is a schematic diagram showing the integration of an opticalgrating into a flow velocity measurement system;

FIG. 6 is a block diagram illustrating the stages of processing thesignal from a light sensitive detector for the purpose of objectvelocity measurement;

FIG. 7A illustrates a graph of a typical photodetector signal beforebandpass filtering;

FIG. 7B illustrates a graph of a typical photodetector after bandpassfiltering;

FIG. 8 is a schematic representation illustrating the operation of a TDIdetector;

FIG. 9 is a schematic diagram of a flow imaging system including a flowvelocity measurement system delivering timing to the TDI detector;

FIG. 10 is a block diagram of the structure of a TDI detector and theassociated subsystems of the flow imaging system;

FIG. 11 is a schematic diagram of a flow imaging system in which objectvelocity measurement is performed upstream from image acquisition;

FIG. 12 is a schematic diagram of a cell sorting apparatus including theelements of a velocity measurement system controlling a droplet chargingsystem;

FIG. 13 is a block diagram illustrating a first embodiment of afrequency domain velocity measurement system in which objects are movedthrough a FOV on a support;

FIG. 14A is an image of beads, ruling, and an adjustable slit acquiredby inserting a beam splitter, lens, and detector after the ruling,placing a light source behind the slide, and opening the slit forclarity;

FIG. 14B is a scattered light image with the slit of FIG. 2A closed downto 200 microns (40 microns in object space) and with the beads movingacross the field of view at 20 mm/s, while data was acquired forapproximately one second;

FIG. 15 is a graph illustrating experimental results for a closed-loopDC servo driven stage that drives 7.9 microns beads affixed to amicroscope slide at 20 mm/s;

FIG. 16 is a graph of showing experimental results comparing commandedvelocity to measured velocity, for the FDVM and encoder;

FIG. 17 is a graph of experimental results showing the linearity of thecommanded velocity and measured velocity, for the FDVM and encoder;

FIG. 18 is a TDI image captured using frequency-domain velocityfeedback;

FIG. 19 is a block diagram of common components of flow velocitymeasurement systems, in accord with the present invention that are underthe control of a system controller;

FIG. 20 is a block diagram illustrating the steps of the signalprocessing and velocity computation for a second preferred embodiment ofthe present invention;

FIG. 21 is a block diagram illustrating the steps comprising the signalprocessing for the embodiment of FIG. 20;

FIG. 22 is a graph of an exemplary spectrum of an unmodulatedphotosensor signal for a single object;

FIG. 23 is an enlarged view of the graph of FIG. 22, illustrating thesignal peak;

FIG. 24 is a block diagram illustrating the steps employed by thesupervisory program for controlling the second embodiment of the presentinvention;

FIG. 25 is a block diagram of a double sideband receiver for use in athird embodiment of the present invention;

FIG. 26 schematically illustrates a modification of the spectrum of anexemplary photosensor signal by a baseband converter;

FIG. 27 schematically illustrates a modification of the spectrum of theexemplary photosensor signal of FIG. 26 by a baseband converter;

FIG. 28 schematically illustrates an analysis of the magnitude and phaseseries of the exemplary photosensor signal of FIG. 26 by computations onthe I and Q baseband signals;

FIG. 29 schematically illustrates an application of a phase unwrappingalgorithm to the phase series of the exemplary photosensor signal ofFIG. 26, to provide a monotonic phase series;

FIG. 30 is a block diagram illustrating the steps employed in the phaseunwrapping algorithm;

FIG. 31A is a graph showing a magnitude threshold being applied to asignal representing the monotonic phase series of FIG. 29, to reduce theeffects of random noise;

FIG. 31B is a graph showing the result of employing the magnitudethreshold of FIG. 31A before computing the fractional frequency from themonotonic phase series of FIG. 29;

FIG. 32 is a block diagram of the signal processing and velocitycomputation steps for the third embodiment of the present invention;

FIG. 33 schematically illustrates a computational modification of thespectrum of I and Q baseband signals to provide upper and lower sidebandsignals;

FIG. 34 is a block diagram illustrating the steps comprisingsegmentation and analysis of objects for the third embodiment of thepresent invention;

FIG. 35A is a graph illustrating the summation of the baseband frequencyand the local oscillator frequency;

FIG. 35B is a graph illustrating the conversion of the sum of FIG. 35Ato a velocity;

FIG. 36 is a block diagram illustrating the steps employed by asupervisory program for controlling the third embodiment of the presentinvention;

FIG. 37 is a graph of the sum of the upper sideband power and the lowersideband power for a broad sweep of the local oscillator frequency;

FIG. 38 is a graph showing the transition of power from the uppersideband to the lower sideband for a narrow sweep of the localoscillator;

FIG. 39 schematically illustrates the convolution of two signalsgenerated by a conventional optical grating of uniform pitch;

FIG. 40 schematically illustrates the design of a conventional opticalgrating and its alignment to the Gaussian profile of the illuminationbeam;

FIG. 41 schematically illustrates the design of an optical grating withlinearly swept pitch and its alignment to the Gaussian profile of theillumination beam;

FIG. 42 schematically illustrates the convolution of two signalsgenerated by an optical grating having a linearly swept pitch;

FIG. 43 is a schematic diagram of a velocity measurement system usingstacked gratings of nonuniform pitch, in accord with a fourth embodimentof the present invention;

FIG. 44 schematically illustrates the alignment of images of twogratings of nonuniform pitch relative to the Gaussian beam profile ofthe illumination beam;

FIG. 45 schematically illustrates the convolution of signals from twophotosensors using the stacked nonuniform gratings of the fourthembodiment of the present invention;

FIG. 46 is a graph of an expanded correlogram for the signals generatedby the stacked nonuniform gratings;

FIG. 47 is a block diagram broadly illustrating the steps required forsignal processing and velocity computation in accord with the fourthembodiment of the present invention;

FIG. 48 is a block diagram illustrating detailed steps for theprocessing of a signal segment of the fourth embodiment of the presentinvention;

FIG. 49 schematically illustrates the concept of the convolution of afirst signal by a second similar but delayed signal;

FIG. 50 is a block diagram illustrating the logical steps implemented bya supervisory program for controlling the fourth embodiment of thepresent invention;

FIG. 51 illustrates differently labeled calibration beads imaged by apreferred flow imaging system;

FIG. 52 illustrates calibration beads imaged by a preferred flow imagingsystem;

FIG. 53 is a schematic diagram illustrating a preferred fluidic systememployed to introduce calibration into a flow imaging system in accordwith the present invention; and

FIG. 54 is a block diagram illustrating overall steps for usingcalibration beads to enhance the performance of a flow imaging system,in accord with the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT

Overview of the Present Invention

It should be understood that the present invention relates to the use ofcalibration beads to enhance the performance and reliability of flowimaging systems, particularly flow imaging systems that employ TDIdetectors and which require the synchronization of the TDI detectors toobjects in flow. Details of the use of calibration beads can be foundbelow in a section entitled “The use of calibration beads in flowimaging systems.” In order to provide an understanding of preferredimaging systems, and preferred methods of determining the velocity ofobjects in flow in such imaging systems, the following descriptive text,relating to FIGS. 1-50, is provided. If the reader desires, he mayproceed to the descriptive text relating to FIGS. 51-53, which relatespecifically to the use of calibration beads.

Preferred Flow Imaging Systems and Preferred Velocity Determination

In the present invention, moving objects are illuminated and light fromthe objects is imaged onto a detector after passing though an opticalgrating. The optical grating comprises a plurality of transparent andopaque bars that modulate the light received from the object, producingmodulated light having a frequency of modulation that corresponds to thevelocity of the object from which the light was received. Preferably,the optical magnification and the ruling pitch of the optical gratingare chosen such that the bars are approximately the size of the objectsbeing illuminated. Thus, the light collected from cells or other objectsis alternately blocked and transmitted through the ruling of the opticalgrating as the object traverses the interrogation region, i.e., the FOV.The modulated light is directed toward a light sensitive detector,producing a signal that can be analyzed by a processor to determine thevelocity of the object.

The present invention has been developed as four distinct preferredembodiments. First and second embodiments employ a first optical gratingand a frequency domain velocity measurement based signal processingtechnique. A third embodiment also employs the first optical grating,but uses a time domain velocity measurement (TDVM) based signalprocessing technique. A fourth embodiment employs the first and alsoincludes a second optical grating to determine velocity using the timedomain based signal processing technique. The differences in the firstand second embodiments are that the first embodiment is specificallydirected to analyzing objects that are deposited on a support that ismoved through a FOV, while the second embodiment applies the samegeneral processing technique to determine the velocity of objects thatare entrained in a fluid flow through the FOV. Details of these specificembodiments are provided below, after a brief discussion of the conceptsgenerally applicable to all of the embodiments.

The present invention can be used with any of the various illuminationand light collection configurations illustrated in FIGS. 1, 2, and 3.However, those configurations should not be considered limiting on thescope of the invention, and are provided merely as exemplaryconfigurations. Each Figure shows a light source, objects in motion(preferably objects entrained in a flow of fluid) illuminated by thelight source, and a velocity detector for receiving light from theobjects. The light source may be a laser, a light emitting diode, afilament lamp, or a gas discharge arc lamp, and the system may includeoptical conditioning elements such as lenses, apertures, and filtersthat are employed to deliver one or more desired wavelengths of light tothe object with an intensity required for detection of the velocity (andoptionally, one or more other characteristics of the object). Thevelocity detector includes a light sensitive detector (not separatelyshown in these figures) comprising, for example, a photomultiplier tubeor a solid-state photodetector, and one or more other opticalconditioning elements such as a lens, aperture, and/or filter, todeliver the modulated light to the light sensitive detector (also notseparately shown in these figures).

FIG. 1 illustrates the configuration of a system 10 a that employs lightscattered by objects 18 traveling through a flow tube 16. An angle 20(designated as angle θ) between the beam axis of an illuminator 12 andan acceptance axis of a velocity detector 14 may be adjusted so thatlight scattered from the objects is delivered to the velocity detector,for a particular scatter angle. The intensity profile of the scatteredlight is a function of the ratio of the size of the scattering elementsto the wavelength of the incident light. A relatively large number ofscattering elements may be present in/on the object, and angle θ may beadjusted for the detection of scattered light from elements within adesired size range for the elements.

FIG. 2 illustrates the configuration of a system 10 b that uses lightemitted by objects 18 traveling in flow tube 16, in response to theabsorption by the objects of light from illuminator 12. In this case,the detection axis will typically be orthogonal to the illumination axisin order to minimize the amount of incident light reaching velocitydetector 14. Typically, a filter or a set of filters (not separatelyshown) will be included in the velocity detector to deliver to the lightsensitive detector only a narrow band of wavelengths of the lighttraveling along a detection path 22 corresponding, for example, to thewavelengths emitted by the fluorescent or phosphorescent molecules inthe object, so that light in the wavelength(s) provided by theilluminator 12 is substantially eliminated.

FIG. 3 illustrates the configuration of a system 10 c utilizing lightfrom illuminator 12 that continues to propagate towards velocitydetector 14 along the axis of illumination; this light is modified byobjects 18 traveling in flow tube 16 by absorption, diffraction, orrefraction. Note that system 10 c is not well adapted for the detectionof light emitted by fluorescence or phosphorescence, due to the highintensity of light emitted by illuminator 12 relative to the intensityof light emitted from objects 18, and that both types of light followthe same path to velocity detector 14. Modification of the light by theobjects caused by absorption can be detected by measuring the intensityof the light incident on the velocity detector. A system of lenses maybe used to restrict the origin of the collected light to a desired fieldin the path of the stream of objects. Modification of the light by theobjects caused by diffraction or refraction may be detected through theuse of a phase contrast method, in which only light subjected to phasemodification by an object is visible, any unmodified light having beencanceled by interference with a reference beam (not separately shown).

In each of the above-noted configurations, the light received by thevelocity detector is modified by objects passing through a FOV. Becausethis FOV is bounded by the profile of the illumination field and by theacceptance window of the velocity detector, it would seem to be possibleto estimate object velocity from the time it takes for the object topass through the FOV. However, the FOV is bounded by gradients ratherthan distinct edges, and it will likely be impractical to maintain thedimensions of the FOV with a high degree of accuracy. This limitation isparticularly true when the objects being illuminated or emitting thelight that is detected are small in size, such as biological cells.

Placing an optical grating in the path of light incident on the velocitydetector establishes a highly precise distance scale for measuringobject velocity. The optical grating concept is illustrated in FIG. 4.This figure shows the positive contrast of the object relative to abackground, which occurs if the light being detected is emitted from theobject by fluorescence or phosphorescence, or is light that is scatteredby the object. Preferably, in the optical grating shown, the opaque andtransparent bar widths are substantially equal and substantially equalto an average diameter of the objects whose velocity is to be measured.This matching of the optical grating to the object size increases thelight modulation amplitude, but it should be noted that the opticalgrating will provide the desired modulating function over a wide rangeof object size relative to the optical grating pitch.

FIG. 4 includes shows four snapshots 24 a-24 d of an optical grating 26,at equally spaced sample times, t₁-t₄, and a signal amplitude 28incident on the light sensitive detector at those times. Note that eachgrating 26 includes alternating opaque zones 34 and transparent zones 32of uniform pitch. The passage of the light emitting object 30 across oneof the transparent zones 32 in the optical grating causes the amplitudeto increase as the exposed area of the object increases, and then todecrease as the object moves behind one of opaque zones 34. In the idealcase, only direct light from objects would reach the detector.Typically, however, some scattered light or light from strayfluorescence will continuously pass through the transparent zones,creating a constant bias or offset in the light sensitive detectoroutput signal.

FIG. 5 shows an optical system 10 d that illustrates how the opticalgrating-based detection system might be implemented for the case inwhich the objects emit photons through the process of fluorescence. Alens 36 creates a focused illumination field at a wavelength λ₁ in a FOV38. Fluorescence in an object 18 a caused by this illumination resultsin photons being emitted by the object omni-directionally at a longerwavelength, λ₂. Some of these emitted photons are collected by lens 40on the detector axis. An emission filter 42 is used to reject light atwavelengths other than λ₂. Lens 40 and a lens 44 create a focused imageof the object on an optical grating 46. It would be possible to generatea conjugate image of the object and the optical grating at a camera, inwhich case the camera would produce well-focused images of objectspassing across the sharp boundaries of the optical grating, as shown inFIG. 4. However, accurate periodic sampling of the modulated lightproduced as light from the moving object passes through the opticalgrating is sufficient for making the velocity measurement, and the extracomplications of capturing and analyzing images is eliminated. In thepreferred approach, a lens 48 is used to collect the light transmittedby the optical grating and deliver it to a photodetector 50. It shouldbe noted that other optical elements, such as concave mirrors, can beused in place of the lenses discussed above.

In a preferred application, objects 18 are preferably biological cellsentrained in a fluid. The fluid is confined to a narrow column passingthrough FOV 38 of the optical system by hydrodynamic focusing with aflow sheath. The cells are kept within the depth of field of lenses 40and 44, assuring good focusing and, therefore, a modulation amplitudesufficient for the determination of the velocity of the object.

Light from a single object moving through the FOV at a uniform velocitywill, when modulated by the optical grating, have a frequency directlyproportional to the velocity, as defined by the following relation:$f = \frac{v}{s}$

where:

f=frequency (Hz)

s=grating pitch (microns)

v=velocity (microns/sec).

The amplitude of the signal generated at the photodetector by light froma single object will follow the contour of the illumination field. Ifthe illumination field profile has a Gaussian shape, for example, thesignal is described by the equation:x(t) = A₀^(−(t − t_(p  k))²/τ²)^(j2  π  f(t − t₀)) + A_(L)

where:

A_(O)=peak amplitude

A_(L)=leakage amplitude from stray light

t_(pk)=time of arrival at peak of illumination field

τ=envelope decay constant

t₀=time of arrival at edge of grating image.

FIG. 6 shows a typical embodiment of a photodetector signal conditioningand capture system. A central feature of this system is the use of abandpass filter 54 to filter the signal from the photodetector 50 (afterthe signal is amplified). The purpose of this bandpass filter is toreject a direct current (DC) component, A_(L), and to eliminate anyfrequencies above a Nyquist limit, ƒ_(samp)/2, of an analog-to-digitalconverter (ADC) 56, where ƒ_(samp) is the highest light modulationfrequency of interest. A variable-gain amplifier 52 is used to adjustthe amplitude of the signal to match the dynamic range of ADC 56. Thedigitized signal is delivered to a digital signal processor 58 foranalysis. Digital signal processor 58 can comprise a programmedcomputing device (e.g., a microprocessor and memory in which machineinstructions are stored that cause the microprocessor to appropriatelyprocess the signal), or an application specific integrated circuit(ASIC) chip that carries out the processing, or a digital oscilloscopethat includes such signal processing capability. FIG. 7A shows anexemplary unfiltered photodetector signal 60 generated by light from asingle object passing through the optical grating field, while FIG. 7Bshows an exemplary filtered photodetector signal 62, after applicationof bandpass filter 54 (see FIG. 6) to the signal.

As noted above, the present invention includes four distinct preferredembodiments. Those four preferred embodiments employ three differenttechniques for analyzing the signal from the photodetector, to deliveraccurate velocity estimates for the objects. The different signalprocessing methods are described in detail below.

As noted above, one preferred use of the velocity measurement in thepresent invention is to provide timing signals to optical systems thatdetermine characteristics of small moving objects, such as a flowcytometer. In non-imaging photomultiplier tube (PMT) instrumentscommonly known as flow cytometers, estimates of flow velocity are usedfor correcting measurements that depend on signal integration time andto accurately delay the sorting of a cell after its analysis. Theoptical grating-based velocity detection methods can be used to improvethe accuracy and reliability of such flow cytometric measurements andthe purity of sorted cell samples by providing a more accurate flowvelocity estimate.

The flow imaging systems disclosed in commonly assigned U.S. Pat. No.6,249,341, issued on Jun. 19, 2001, and entitled IMAGING AND ANALYZINGPARAMETERS OF SMALL MOVING OBJECTS SUCH AS CELLS, as well as in commonlyassigned U.S. Pat. No. 6,211,955, issued on Apr. 3, 2001, also entitledIMAGING AND ANALYZING PARAMETERS OF SMALL MOVING OBJECTS SUCH AS CELLS,demand very accurate measurements of flow velocity for clocking the TDIdetector. The transfer of charge from one row to the next in the TDIdetector must be synchronized with the passage of objects through theflow cell. Note that the specification and drawings of each of these twopatents have been specifically incorporated herein by reference.

The theory of operation of a TDI detector, such as those employed in theabove-noted patent references is shown in FIG. 8. As objects travelthrough flow tube 16 and pass through the volume imaged by the TDIdetector, their images travel across the face of the TDI detector. TheTDI detector comprises a charge coupled device (CCD) array 64, which isspecially designed to allow charge to be transferred on each clock cyclein a row-by-row format, so that a given line of charge remains locked toor synchronized with a line in the image. The row of charge is clockedout of the array into a memory 66 when it reaches the bottom of thearray. The intensity of each line of the signal produced by the TDIdetector corresponding to an image of an object is integrated over timeas the image and corresponding signal propagate over the CCD array. Thistechnique greatly improves the SNR of the TDI detector compared tonon-integrating type detectors—a feature of great value when respondingto images from low-level fluorescence emission of an object, forexample.

The operation of the TDI detector can be understood from FIG. 8 byobserving the traversal of object 68 across the region imaged by CCDarray 64 and the history of the charge produced in response to the imageof object 68 by CCD array 64. The charge is transferred from row to rowin the array as the image of the object travels down the array. When arow of charge reaches the bottom of the array, it is transferred intoscrolling memory 66, where it can be displayed or analyzed. In FIG. 8,objects 70 and 72 traverse flow tube 16 ahead of object 68, while anobject 74 traverses flow tube 16 after object 68. Proper operation ofthe TDI detector requires that the charge signal be clocked down the CCDarray in synchronization with the rate at which the image of the objectmoves across the CCD array. An accurate clock signal to facilitate thissynchronization can be provided if the velocity of the object is known,and the present invention provides an accurate estimate of the objectsvelocity, and thus, the velocity of the image over the CCD array of theTDI detector.

FIG. 9 shows the integration of the velocity detector into a TDI-basedobject imaging system 10 e. The signal from photodetector 50 isprocessed by a signal processor 84, and may optionally carry outfunctions such as amplification and filtering. Additional details of thesignal processing are provided below. Preferably, signal processor 84comprises a programmable computing device, but an ASIC chip or digitaloscilloscopes can also be used for this purpose. The frequency of thephotodetector signal is measured and the velocity of object 18 a iscomputed as a function of that frequency. The velocity is periodicallydelivered to a TDI detector timing control 82 to adjust the clock rateof a TDI detector 80. The TDI detector clock rate must match thevelocity of the image of the object over the TDI detector to within asmall tolerance to minimize longitudinal image smearing in the outputsignal of the TDI detector. The velocity update rate must occurfrequently enough to keep the clock frequency within the tolerance bandas flow (object) velocity varies. Note that a dichroic beam splitter 76has been employed to divert a portion of light from object 18 a tophotodetector 50, and a portion of light from object 18 a to TDIdetector 80. An imaging lens 78 focuses an image of object 18 a onto TDIdetector 80.

FIG. 10 shows how a velocity detection system 88 is preferably employedby TDI detector timing control 82 (shown in FIG. 9). Note that velocitydetection system 88 can be configured as shown in FIG. 9, or provided inother configurations (such as shown in FIG. 5). Velocity detectionsystem 88 provides a clocking signal indicative of the velocity of aflow or of objects in a flow for synchronizing the movement of images ofthe objects over a TDI detector with the movement of charge responsiveto the images. The TDI detector is preferably included in a flowcytometry system, although other applications of the present inventionare contemplated. Preferably, the velocity detection system iscontrolled by a CPU 90, which executes a velocity detection supervisorprogram defined by machine instructions that are stored in memory (notseparately shown). Note that CPU 90 can also be employed to carry outthe signal processing function of velocity detection system 88. Theclocking of the charge through a TDI detector 94 is accomplished by avertical shift controller 92 and a horizontal readout controller 96,both of which are driven by a TDI detector timing control system 82. Thevelocity detection system 88 passes a clock frequency command to TDIdetector timing control system 86 to set a rate at which rows of chargeare shifted down the TDI detector array. Detector timing control system86 synchronizes horizontal readout controller 96 with vertical shiftcontroller 92.

The image information leaves TDI detector 94 as an analog signal, whichis then amplified with an amplifier 98 and digitized by an ADC 100. TheADC output is stored in a memory 102 under the control of timing controlsystem 86, where it can be accessed for display and analysis.

Another embodiment of a TDI-based flow imaging system 10 e is shown inFIG. 11. This embodiment is intended to address the problem ofsynchronizing the TDI detector to individual objects traveling atdifferent velocities, as may be the case in systems with poorhydrodynamic focusing. In TDI-based flow imaging system 10 f, thevelocity measurement is performed upstream of the point where imagecapture occurs. Velocity measurements are updated sufficiently rapidlyto be passed forward to the TDI detector timing controller in time forthe TDI detector clock (not separately shown) to be set to match thevelocity of an image of a particular object moving across the TDIdetector. The configuration of flow imaging system 10 f is similar tothat shown in FIG. 5, except that FOV 38 for velocity detection isseparate from a FOV 38 a used for TDI image acquisition. Imaging system10 f uses a separate light source 12 a, separate lenses 36 a, 40 a, 44 aand a separate filter 42 a disposed in the collection path for lightfrom the objects that is directed to TDI detector 80. The photodetectorsignal is processed using a signal conditioning component 104 and a FFTbased fast velocity calculator 105 that is sufficiently fast to delivernew velocity estimates for objects to timing controller 82 in less timethan required for the objects to travel from velocity measuring FOV 38to imaging FOV 38 a. Note that in imaging system 10 f, signal processingblock 84 of FIG. 10 is separated into signal conditioning component 104and fast velocity calculator 106.

Accurate cell velocity measurements can also be employed to increasesort purity in droplet-based flow sorters. Such systems are typicallyPMT-based flow cytometers equipped to sort cells by their lightscattering characteristics or fluorescence emission. In such systems,the characterization of a cell is performed just as the liquid carryingthe cell leaves the droplet-forming nozzle. Based on an opticalmeasurement, the unbroken part of the stream is either charged or notcharged before the droplet containing the cell breaks free from thestream. An electrostatic field is used to deflect the charged dropletsinto a container separate from the container catching the unchargeddroplets.

FIG. 12 illustrates an instrument in which the optical grating-basedvelocity detection system of the present invention is used both tosynchronize a TDI detector for capturing images and for timing thedroplet charging system. In system 10 g, both velocity detection andimage capture are accomplished in common FOV 38. Images from TDIdetector 80 are delivered to a high-speed cell classifier 110. Theclassifier searches the images for objects of interest. Once such anobject has been found, the classifier automatically decides on the basisof the characteristics of that object whether the object should besorted into a container 130 or a container 132. If the decision is madeto place an object into container 132, the classifier triggers thecharging system, which comprises a time delay operator 112, a chargepulser 114, and a charge collar 116. The time delay used by time delayoperator 112 is set to match a transit time of an object from FOV 38 toan attached droplet 120, according to the velocity measurement performedin signal processing block 84 of the velocity detector. Note that asdescribed above, the velocity detector includes optical grating 46,photodetector 50, and signal processing block 84. Charged droplets 122are deflected into container 132 by the static electric field betweenelectrodes 124 and 126. The distance between the optical sensing regionand the charge collar is made long enough to provide sufficient time forimage-based object classification to reach completion. In this way, alarger and more complex set of features can be used in the sortingdecision than would be the case in conventional cell-sorting flowcytometers.

FDVM of Velocity of Objects on a Support

The first and second embodiments are directed to FDVM methods thatconvert light from cells or other objects into an amplitude modulated(AM) signal with a characteristic frequency that is proportional tovelocity. Any number of cells traveling at the same velocity, e.g., in afluid flow, can be in the sensitive region simultaneously, and themodulated light produced in response to the motion of each will have thesame fundamental frequency, differing only in phase. Unlike the priorart time-domain methodology, the FDVM method requires no synchronizationand is highly tolerant of variability in the fine structure of thetime-based waveform generated by the cells.

In the FDVM method, moving luminescent or illuminated cells are imagedonto a ruling of transparent and opaque bars to generate an amplitudemodulated light signal. The optical magnification and ruling pitch arechosen such that the bars are approximately the size (e.g., diameter) ofthe cell. The pitch of the ruling used in the optical grating isuniform. Therefore, the light collected from cells is alternatelyblocked and transmitted through the ruling as the cell traverses thesensitive region. The modulated light is directed toward a detector,producing an analog output signal with a fundamental frequencyproportional to the cell velocity. The analog signal is converted todigital samples. An FFT algorithm decomposes the resulting digitalsignal into spectral peaks in the frequency domain, which are processedto determine the velocity. A first FDVM embodiment is directed to amethod in which objects are deposited upon a support, and the support ismoved through the FOV. A second FDVM embodiment is directed to a methodin which objects are entrained in a fluid that is caused to flow throughthe FOV.

A block diagram of the first FDVM embodiment of a velocity detectionsystem is shown in FIG. 13. Beads are deposited on a slide 131 that isdriven through the FOV. For an initial feasibility study, the objectsemployed comprised 7.9 μm diameter beads (purchased from BangsCorporation) fixed to a moving microscope slide. Movement of the slidethrough the FOV was produced by mounting the slide on a closed-loop DCservo stage 133 (available from Newport Corporation), and the samplespeed was monitored using a one micron resolution linear encoderincluded in the stage. The stage had a maximum velocity of 100 mm/s,controlled using a proportional-integral-derivative (PID) control system(available from National Instruments Corporation). The linear encoderindependently monitored the movement of the slide (and hence, themovement of the beads on the slide) to provide comparative dataavailable to confirm the accuracy and precision of the FDVM velocitydetection system. While it is expected that determining the velocity ofobjects entrained in a fluid will have widespread application, it isalso anticipated that the moving support (slide) embodiment will also beuseful, particularly for objects that cannot be easily or stablyentrained or suspended in a fluid.

The sample beads were illuminated with light from a diode laser 134(from Thor Labs, Inc.) so that light striking the beads was scatteredinto the optical collection system. The moving sample image wasprojected at approximately 5× magnification by an objective 136 and alens 138, onto an optical grating 135 having a ruling of 50 micron bars(available from Gage Technologies), oriented at right angles to themotion of the sample. The ruling and sample were then imaged together onan adjustable slit 139 by an imaging lens 137, disposed to simulate thefield of view of a flow-based instrument. The light passing through theslit was then collected by a lens 141 and directed onto a PMT 148(Hamamatsu Corp., Model 5783-01) such that the aperture of the opticalsystem was imaged onto the PMT (143). In this manner, there was nomovement of the signal across the PMT as the bead images traversed theruling.

The signal processing portion of this embodiment of a velocity detectionsystem is also depicted in FIG. 13. The signal from the PMT wasamplified and high-pass filtered through an amplifier/filter 150(Stanford Research, Model SR570). The filtered signal was then digitizedusing an analog-to-digital converter 147, and the digitized signal wasprocessed using an FFT processor 149 to produce a frequency spectrumwith a well-defined peak at the frequency corresponding to the velocityof beads. The FFT spectrum was smoothed using a moving average filter151 and the zero crossing of the derivative of the smoothed FFT spectrumwas determined in processing blocks 153, 155, and 157. All signalprocessing was performed on a digital storage oscilloscope (from LeCroyCorp.). The velocity of the objects on the slide was then calculated bytaking the product of frequency defined by the zero crossing, the rulingspacing, and the inverse of the magnification in a velocity conversionblock 159. The precision of the measurement was enhanced by linearlyinterpolating between the derivative data points to better define thezero crossing frequency.

FIG. 14A shows an image of the beads, ruling, and adjustable slit. Theimage was acquired by inserting a beam splitter, lens and detector afterthe ruling, placing a light source behind the slide, and opening theslit for clarity. The beads were magnified 4.92× before being imaged onthe ruling, which had a line width of 50.8 μm (9.842 lp/mm). FIG. 14B isa scattered light image in which dark field illumination was employed,and the slit closed down to 200 microns (40 microns in object space), asit was during data acquisition. In operation, the motorized stage movedthe beads across the field of view at 20 mm/s (left to right in theillustrated example), while data was acquired for approximately onesecond.

Using the methods and apparatus discussed above, data were taken inthree experiments to determine the precision and accuracy of the thistechnique. FIGS. 15, 16, and 17 summarize the results of theseexperiments. In the results of the precision experiment shown in FIG.15, the stage was commanded to move at 20 mm/s in 34 separate runs. Thevelocities measured by the encoder on the stage and by the FDVM methodof the present invention were both recorded and plotted. To calibratethe FDVM method, a correction constant was determined by taking thequotient of the first commanded velocity and the frequency peak producedby the FDVM method. Each subsequent measurement was multiplied by thisvalue. The precision of the FDVM method was determined by calculating acoefficient of variation (CV) for 34 separate runs. By this measure, theprecision of the encoder method is 0.09% and the precision of the FDVMmethod of the present invention is 0.01%, as shown in FIG. 16. Thisexperiment demonstrates that the precision of the FDVM method exceedstargeted performance requirements by a factor of fifty.

It should be noted that the poorer apparent performance of the encodermethod is likely the result of the servo feedback system's internalvelocity calculation. Rather than making one velocity measurement perrun using all 20,000 counts, the servo system makes a velocitymeasurement every 60 counts for the purposes of real-time motioncontrol. The stage feedback system supplied a function to average theindividual velocity measurements within a run. Each point in the encoderprecision plot is therefore the average of 333 individual velocitymeasurements.

The results of the linearity experiment are shown in FIG. 17. The stagewas commanded to move over a velocity range from 5 mm/s to 100 mm/s asspecified in the performance requirements. Velocity measurements weretaken using the FDVM method and the stage encoder. Over this range bothmeasurements produced highly correlated R² values of unity with slopesof 1.0002 and 1.0007 for the FDVM method and stage encoder,respectively. These results demonstrate that the FDVM method of thepresent invention has good linearity over a range of velocitymeasurements exceeding an order of magnitude.

FIG. 18 is an image captured using a TDI detector configured to view thesample slide, as in FIG. 2. The TDI detector or detector capturesunblurred imaged of objects, which move at the same rate as the chargethat is clocked across the chip during imaging. Because the TDI detectoris located behind the stationary ruling, the ruling has blurred acrossthe entire field of view. The ruling is responsible for some imagedegradation as each bead image traverses the entire ruling during theimaging process. The TDI detector's pixel clock was generated using thevelocity determined by the FDVM method of the present invention.Although a comprehensive analysis of the image has not been performed,it is apparent that the velocity accuracy is sufficient to prevent imageelongation in the horizontal axis of motion of the TDI detector.

The goals of the feasibility study employing beads on a slide were todevelop a velocity detection system with high precision, high accuracy,good linearity and tolerance of wide variations in sample density. Thesegoals were exceeded and the system was successfully used to captureimages using a TDI detector. The 0.5% feasibility requirements were setto ensure less than one pixel blur when using the velocity detectionsystem in concert with a 100 stage TDI detector. In fact, thefeasibility system demonstrated precision, accuracy, and linearitybetter than 0.05% and therefore can be used with a 1000 stage TDIdetector. In the context of the cell analysis system being developed inwhich the present invention will be included, more stages enable theimage to be collected over a larger field of view, thereby increasingthe integration time and the sensitivity of the instrument. Conversely,if the field of view is held constant, the pixel size can be reduced,increasing the spatial resolution of the instrument. Accurate velocitydetection is also beneficial for cell sorting, where knowledge of thestream velocity can be used to actively adjust drop delays to compensatefor drift over the course of an experiment.

Supervisory Control of Velocity Measurement Systems

In all embodiments, the present invention entails the steps of (1)formation of images of the objects of interest focused in the plane ofthe optical grating, (2) delivery of the modulated light transmitted bythe optical grating to the surface of a photosensitive detector, (3)conversion of the modulated light signal to an electronic signal, (4)signal acquisition and processing, and (5) velocity determination.Preferably, one or more of these operations will be brought under thecontrol of supervisory software by interfacing the velocity measurementsystem with a general purpose computer or other computing device.

FIG. 19 is a functional block diagram of a signal acquisition andanalysis system controlled by a supervisor program 142, preferablyexecuted by a CPU 144 of a programmable computer. Alternatively,supervisor program 142 can be executed by a corresponding CPU in adigital oscilloscope, or by an ASIC chip. A signal from photodetector 50is processed via signal acquisition and conditioning process block 104,velocity computation process block 105 (note that fast velocitycomputation process block 106 of FIG. 11 employs FFT processing, while,velocity computation process block 105 is more generalized, and canemploy other types of signal processing, as opposed to just FFT signalprocessing), and a velocity running average computation block 140. Basedon the velocity of an object that was determined, supervisor program 142provides a clocking signal to a timing generator 146 that controls TDIdetector 80. Note that the TDI detector is only one device exemplarydevice that can employ the present invention. It is expected that othertypes of devices can be provided a timing signal in this manner.

FDVM of Objects in Flow

In the second preferred embodiment of the present invention, the lightfrom the objects is modulated by the optical grating, and the modulatedlight is sensed by the photodetector shown in FIG. 5. The functionalblocks used to capture, process, and analyze the photodetector signalare shown in FIG. 20. Details of a multistage digital signal processingoperation 150 for processing the incoming signal are illustrated in FIG.21. The entire system, shown in FIG. 20, operates as a pipelineprocessor in which blocks of signal samples, and farther down thepipeline, parameters calculated from the blocks of samples are passedfrom one operation to the next in an uninterrupted sequence.

As explained above in connection with FIG. 6, the signal produced byphotodetector 50 is input to variable gain amplifier 52. The output ofvariable gain amplifier 52 is filtered by bandpass filter 54 to removethe DC bias caused by stray light and by bias voltage of variable gainamplifier 52 and to eliminate frequencies above the Nyquist limit of ADC56, this limit being equal to one-half of the sample frequency,ƒ_(samp). After bandpass filtering, the signal swings in both thepositive and the negative direction around a mean value of zero.

ADC 56 samples the signal at frequency ƒ_(samp) and encodes the signalinto a series of digital samples representing the signal amplitude ateach sequential sample time. The converter must retain the bipolarnature of the signal by encoding the signal into a number system such asthe 2's complement format, which accommodates both positive and negativenumbers.

As an alternative, ADC 56 could be placed immediately after variablegain amplifier 52 and bandpass filter 54 could be implemented as adigital instead of an analog type filter. The signal applied to the ADCwould then be unipolar, and the signal would be encoded into a simplebinary number for processing.

In a multistage digital signal processing block 150, one signal segmentis processed. Referring now to FIG. 21, in block 150, a sequentialseries of signal samples of predetermined length, N_segment, is analyzedto extract the mean frequency of the photodetector signal and to convertthat frequency to an estimate of the object velocity. The first step 160of this operation is to capture the desired number of samples for thesegment from the incoming signal.

Optionally, the next signal processing step 162 applies an amplitudewindowing or apodization function to the signal segment that was justcaptured. Without this apodization step, the abrupt truncation of thesignal at the ends of the segment would spread the frequency componentsin the signal over a collection of sidebands, the frequency andamplitude of which conform to the Fourier Transform of a rectangularwindow, a sine function in frequency space. Those skilled in the artwill recognize that apodization by a function such as the Hamming,Hanning, or raised cosine functions, for example, will substitute asmoother sideband structure of lower amplitude in place of the sinefunction. The reduced sideband amplitude improves the accuracy ofestimating the mean frequency of the photodetector signal, especially inthe presence of velocity dispersion. Alternatively, apodization can beperformed optically by illuminating the FOV using a smooth-shoulderedintensity profile, thereby eliminating the abrupt truncation of thesignal at the edges of the FOV. In still another method of apodization,the ruling may be superimposed on a varying transmission gradientfilter, which smoothly attenuates the optical signal at the edges of theFOV.

The optional apodization operation in step 162 is followed by executionof a complex FFT function in a block 164. The complex FFT algorithm isutilized by applying the signal as the real input to the FFT andapplying an array of length N_segment with all values set to zero as theimaginary input to the FFT. Alternatives exist for utilizing the FFTalgorithm more efficiently for real-number transforms, but those methodsinvolve packing the input arrays in special patterns and unpacking theoutput arrays. Such methods can be used, however, to save processingtime.

The resulting complex-number spectrum is then applied to an operator ina block 166 that converts the real and imaginary parts of the spectrumto the magnitude of the spectrum using the following relation:$M_{j} = \sqrt{{Re}_{j}^{2} + {Im}_{j}^{2}}$

where:

M_(j)=magnitude at sample j

Re_(j)=real part of sample j

Im_(j)=imaginary part of sample j.

Typically, this operation will be implemented using a look-up table orfast approximation algorithm to speed execution.

Typically, the velocity of the objects to be imaged with a TDI detectorwill deviate very little from the mean velocity. Consequently, the powerin the spectrum of the signal from the photodetector will beconcentrated in a narrow band around a mean frequency, and the spectrumoutside this band can be ignored in the computation of mean frequency.

FIG. 22 is the spectrum produced by a sinusoidal burst with centerfrequency 2500 Hz and a Gaussian-shaped envelope. A single objectpassing through the flow cell of a flow imaging system such as thatillustrated in FIG. 9 might produce such a signal. A simple peakdetector is applied to the spectrum in FIG. 22 in a block 168 of FIG.21, to localize a region 178 of the spectrum of interest for analysis.FIG. 23 shows a segment 180 of the spectrum centered on the peak of thespectrum of the signal burst. This segment contains nearly all of thepower in the spectrum and can be utilized for computing the meanvelocity.

In a block 172 of FIG. 21, the mean velocity is determined by findingthe mean frequency on the scale of FFT bins from the signal segment ofFIG. 17. The following relation describes this calculation:$\overset{\_}{n} = \frac{\sum\limits_{n = a}^{b}\quad {{nS}(n)}}{\sum\limits_{n = a}^{b}{S(n)}}$

where:

a, b=endpoints of sample in window

S=magnitude of spectrum

{overscore (n)}=mean FFT bin (floating point).

The mean frequency in Hz is computed from the mean FFT bin number asfollows:${\overset{\_}{f}\quad ({Hz})} = {2 \cdot \overset{\_}{n} \cdot {f_{Nyq}/N}}$

where:

ƒ_(Nyq)=Nyquist frequency

N=FFT length

{overscore (ƒ)}=mean frequency.

Finally, a mean velocity 176 is found by multiplying the mean frequencyby the optical grating pitch:

{overscore (ν)}={overscore (ƒ)}·s

where:

s=grating pitch (microns)

{overscore (ν)}=velocity (microns/sec).

The velocity detection system must accommodate the possibility that verylittle or no signal was captured in the signal segment being processed.In the present embodiment of the invention, the magnitude of thespectrum integrated over the local region around the peak of thespectrum is computed in a block 170, as follows:$M_{sig} = {\sum\limits_{n = a}^{b}{S(n)}}$

where:

M_(sig)=integrated magnitude, this segment

S=magnitude of spectrum

a=first bin of local region around peak

b=last bin of local region around peak.

As will be appreciated from the description of the supervisor programfor the velocity detection system that follows, a running record of themean velocity will be maintained by the supervisor and used to establishthe boundaries, a and b, of the local region for computing the meanfrequency and an integrated magnitude 174.

Referring back to FIG. 20, the mean velocity and integrated signalmagnitude from operation 150 are applied to a decision step 152. Thedecision step 152 yields two possible outcomes: (1) the SNR of thesegment being processed is adequate for computing the velocity, so thatthe new velocity value is added to a velocity list 154, or (2) the SNRis inadequate (below a predefined value) for computing the velocity, andthe velocity list remains unchanged. If a new velocity value is added tothe velocity list, the oldest value on the list is deleted (once thelist is full). Velocity list 154 and a running average calculation in astep 156 deliver a new velocity estimate every time a new signal segmentis processed. The running average velocity is the average over apredetermined number of velocity values, m. The number of values, m,used in the running average can be increased to improve the accuracy ofthe running average or decreased to improve the responsiveness of thevelocity detector to rapid changes in velocity.

The velocity detector must adapt to variations in flow velocity andphotodetector SNR in order to produce accurate and reliable velocityestimates. Supervisor program 142, shown in FIG. 19, is used to controlthe velocity detector and to coordinate the operations of the velocitydetector with those of the rest of the imaging system. FIG. 24 is aflowchart showing the steps implemented by the supervisor program forthe second embodiment of the present invention. The program's threeprincipal outputs, the SNR decision threshold, the spectrum integrationlimits, and the photodetector amplifier gain, are fed back to thevelocity measurement system to optimize its performance.

Operation of the velocity detection system is initiated with aninstrument calibration step 190, in which the noise from thephotodetector channel is determined and analyzed in the absence of anoptical signal. This can be accomplished by turning off the lightsources in the system or stopping the flow of objects through the flowcell. The purpose of the noise measurement is to establish a referenceagainst which the information-bearing signal will be compared forsetting a threshold for accepting or rejecting velocity measurements.

The calibration operation measures the noise level at a plurality ofamplifier gain settings and stores these measurements in a table 192 ofnoise level vs. gain. Table 192 is used to select an initial gainsetting for amplifier 52. As the amplifier gain is varied to regulatethe signal strength during normal operation, the correct noise level forsetting the decision threshold is read from table 192 and applied tothreshold calculator 194. Once the calibration operation has beencompleted, the light source or sources are turned on, and objects areintroduced into the flow stream for image acquisition, as shown in astep 196.

The next task of the supervisor program is to search for the peak in thespectrum in a step 198 and set the upper and lower boundaries of thespectral region to be analyzed. In the absence of any a priori knowledgeof the flow speed, this initial search must span the entire range offrequencies in the spectrum, and may entail capturing a number of signalsegments until a strong peak representing a spectral peak frequency 200is found. The location of that peak will be used to set the local regionboundaries, using knowledge of the expected width of the spectrum. Thiswidth is a function of the beam profile of the illumination field, theshape of the apodization function, and the predicted variance of objectvelocities. This information will be understood from the design of theinstrument.

With the photodetector amplifier gain set to a starting value and thedecision threshold and integration limits established, pipelineprocessing of signal segments commences. Each time a segment isprocessed, running average velocity value 158 is added to a list 204 andthe oldest value in the list is deleted. The velocity values in list 204are then averaged in a step 206. This long-time average of the velocityis used in a step 202 in which the boundaries of the local spectralregion to be analyzed are set. The process of regulating the integrationlimits constitutes a feedback control loop in the supervisor program.The response time of this loop can be modified by adjusting the numberof samples maintained in list 204 and averaged in step 206.

The gain of the photodetector amplifier is regulated during systemoperation as well, in order to optimize the SNR of the velocity detectoras specimen characteristics change. The amplifier gain regulation systemof the supervisor program in a step 208 provides for creating thehistogram of each signal segment, counting the number of samplesoccupying a predetermined number of levels near the top of theanalog-to-digital converter output scale in a step 210, maintaining alist of the most recent count results in table in a step 212, andanalyzing that table to generate a gain adjustment in a step 214.

The time of arrival of objects in the FOV of the velocity measurementsystem is a random variable. If the specimen contains a highconcentration of objects, the probability that an object will passthrough the FOV in a given time interval is high, and the count table ofstep 212 will contain many samples useful in setting the amplifier gain.However, if the specimen contains a very low concentration of objects,many of the signal segments processed by the velocity detection systemwill be devoid of signal, and the count values stored in the count tableof step 212 for those segments will be zero. Those skilled in the art ofautomatic gain control systems will recognize this problem as similar tothat of regulating the gain in radio receivers or studio microphoneamplifiers, in which the signal being processed may vary widely inamplitude and be interrupted. The common practice in such cases is touse a “fast attack,” “slow recovery” feedback control system. In such asystem, the sudden arrival of a high-amplitude signal will be met with afast reduction of amplifier gain to prevent saturation. On the otherhand, a prolonged interruption of the signal is met with a slow increasein gain, on the premise that a large signal is likely to arrive soon,but a persistent loss of amplitude, requiring higher gain may haveoccurred. The gain adjustment determined in step 214 will use a “fastattack,” “slow recovery” algorithm to regulate the photodetectoramplifier gain. Determination of running average 158, and steps 208 and210, and their associated feedback control mechanisms will sustainsequential processing of signal segments until terminated by thesupervisor program.

The new gain setting is set in a step 216 for variable gain amplifier 52in regard to noise calibration table 192. The response time of the gaincontrol feedback loop can be modified by adjusting the number of samplesmaintained in table 212 and analyzed in step 214.

TDVM of Objects Using a Single Uniform Pitch Optical Grating

In the third preferred embodiment of the present invention, the lightcollected for velocity measurement is modulated by an optical gratinghaving a substantially uniform pitch and sensed by the photodetectorshown in FIG. 5, just as in the second embodiment. The analysis of thephotodetector signal, however, is performed in the baseband domain.Baseband demodulation is frequently used in communications and othersignal processing applications for receiver tuning and carrierrejection. The fundamental architecture of a baseband demodulator withthe additional capability of splitting the signal into upper and lowersidebands, is shown in FIG. 25 and is referred to herein as “thedouble-sideband receiver.”

As shown in FIG. 25, an incoming signal from the photodetector isapplied to multipliers (or mixers) 222 and 226, which multiply thesignal by two continuous sinusoidal wave functions, called localoscillator signals. The two local oscillator signals are at the samefrequency, but shifted in phase ninety degrees relative to one another.That is, a first local oscillator signal 224, which is the in-phaselocal oscillator signal, is a cosine function, while a second firstlocal oscillator signal 228, which is the quadrature-phase localoscillator signal, is a sine function. The mixers are followed bylowpass filters 230 and 232, which complete the baseband demodulation.

The effect of multiplying the signal from the photodetector by asinusoid of frequency fLO is to offset the spectrum of the incomingsignal upward by fLO, to create sum frequencies, and downward by −fLO,to create difference frequencies. In FIG. 26, a sinusoidal burst withthe spectrum shown in a graph 250 at a center frequency of 1500 Hz isapplied to mixers 252 and 256, driven by local oscillator signals 254and 258, set to 2000 Hz. At the output of the mixers, the centerfrequencies in the spectrum are the difference frequency, 500 Hz, andthe sum frequency, 3500 Hz, as shown in graph 264. Low-pass filters 260and 262 suppress the sum frequencies, leaving only the differencefrequencies in the I(t 268 and Q(t) 266 signals, as shown in a graph270.

The lowpass filter outputs are the I and Q signals, i.e., the in-phaseand quadrature-phase signals. FIG. 27 shows how signals entering andleaving the baseband demodulator might look on an oscilloscope. Eachtime-sample is a complex number representation of the input signal 272,with the I channel 268 representing the real part of the complex signal,and the Q channel 266 representing the imaginary part of the complexsignal. Graph 276 shows the in-phase output signal, while graph 274shows the quadrature-phase output signal. The I,Q pair conveys both themagnitude of the signal and the phase of the input signal 272 relativeto the local oscillators. A time series of I,Q pairs can represent bothpositive and negative frequencies, which derive from frequencies abovethe local oscillator frequency and below the local oscillator frequency,respectively. The time series of I,Q pairs is often referred to as the“analytical signal.”

The magnitude and the phase of the input signal can be calculated fromthe analytical signal using the vector operations shown in FIG. 28. Themagnitude of a signal 282 is computed in a step 278. I(t) and Q(t) arethe Cartesian projections of the vector M(t), therefore M(t) is just thelength of the hypotenuse of a right triangle with the other two sidesbeing I(t) and Q(t). The equation for calculating M(t), then, is:${M(t)} = {\sqrt{( {I(t)} )^{2} + ( {Q(t)} )^{2}}.}$

Accordingly, the angle between the real, I(t), axis and the hypotenuse,M(t), is the inverse tangent of Q(t)/I(t), or:

Φ(t)=arctan[Q(t)/I(t)].

The analytical signal offers a versatile method for tracking thephotodetector signal frequency in the velocity detector. The frequencyat every sample time is found by taking the time derivative of the phaseof the analytical signal in a step 280. However, as seen in a graph 284of Φ(t), the phase is a periodic function. The values π and −π definethe same angle, where the abrupt transitions occur in graph 284.

In order to calculate a phase derivative for each time sample, theperiodic Φ(t) function must be converted to a continuous function. FIG.29 illustrates the unwrapping of the phase of a constant-frequencysignal. The function Φ(t) is shown in a graph 288. A polar plot 286shows a rotating vector representation of a constant amplitude, constantfrequency signal. It can be seen from plot 286 that the phase will makean abrupt transition from π to −π once per period. A phase unwrappingalgorithm 290 senses these transitions and corrects for them to producea monotonically increasing function of phase, as shown in a graph 292.

FIG. 30 describes the phase unwrapping algorithm. In a first step 294,the change of phase from one time sample to the next is computed withoutregard to the values of Φ(n) and Φ((n−1). In the next two stages, steps296 and 298, the presence of a transition across the π,−π boundary issensed and the phase derivative is corrected. The first part of bothsteps 296 and 298 is to detect that the phase has moved from onehalf-plane to the other half-plane. If Φ(n−1) was zero or positive, thenthe transition was from the upper half-plane to the lower half-plane,and the step 296 is executed. If Φ(n−1) is negative, then the transitionwas from the lower half-plane to the upper half-plane, and the step 298is performed. In step 296, a further test is performed to determine ifΦ(t) has changed in the negative direction by more than π. If so, thenthe transition between half-planes was at the π,−π boundary. In thiscase, an offset of +2π is applied to ΔΦ, which eliminates the impact ofthe boundary transition. In a similar manner, if step 298 detects thatthe phase has rotated across π,−π boundary in the clockwise direction,it subtracts 2π from ΔΦ to eliminate the impact of the boundarytransition. Application of the unwrap algorithm to the phase signal fromthe baseband demodulator is used to generate a smooth phase plot,Φ_(m)(t), as shown in graph 292 of FIG. 29. The slope of this plot isthe radial frequency, ω(t).

Any signal generated by a physical system will contain some randomnoise. Because ω(t) is computed using a difference operator, and truerandom noise is uncorrelated from sample to sample, the accuracy of theω(t) calculation will degrade rapidly with decreasing SNR. For thisreason, time samples of ω(t) are accepted into the velocity computationonly if the magnitude of the signal is above a predefined threshold.This concept is illustrated by FIGS. 31A and 31B. In a graph 302 of FIG.31A, a threshold of 0.1 is applied to the magnitude, M(t). The basebandfrequency is computed only for those samples exceeding the threshold,yielding a result like that seen in a graph 308 of FIG. 31B. The term“fractional frequency” 310 used in graph 308 means the frequencyexpressed as a fraction of the Nyquist limit in baseband. The fractionalfrequency can have a value in the range from 0.0 to 1.0.

FIG. 32 shows the signal processing and data pathways for the thirdembodiment. The signal from photodetector 50 is applied to variable-gainamplifier 52, the gain of which is regulated by the supervisor programto optimize SNR. The amplified signal is applied to bandpass filter 54to remove DC offset and to limit the signal bandwidth to preventaliasing. The filtered signal is converted to a sequence of digitalsample by ADC 56. Mixers 222 and 226 and lowpass filters 230 and 232(FIG. 25) are implemented in the baseband conversion of a step 311. Thebaseband signal pair I(n), Q(n) is used for two steps. The first is thegeneration of the upper sideband and lower sideband signals 314 and 322in a step 312. The second is the measurement of the velocity of objectspassing through the flow cell.

FIG. 33 illustrates the generation of the upper and lower sidebandsignals from the I,Q signal pair. An I(n) signal 326 and a Q(n) signal328 are each processed by the Hilbert Transform operator, applied insteps 234 and 236. Those skilled in the art will recognize that thisoperator delays the input signal by π/2 radians (90 degrees). Note thatthe phase rotation is not a time delay, because the rotation is π/2,independent of the frequency of the incoming signal, over a broad rangeof frequencies. However, a time delay is inherent in the HilbertTransform algorithm, and must be matched by time delays 238 and 240. Thefinal stage of the sideband separation is that of summing at node 242the rotated Q(n) signal with the un-rotated I(n) signal to generate anupper sideband signal, USB(n) 330, and of summing at node 244 therotated I(n) signal with the unrotated Q(n) signal to generate a lowersideband signal, LSB(n) 332. Summation at node 242 cancels signalvectors rotating counterclockwise in I,Q plane 286 (see FIG. 29) andreinforces those rotating clockwise in the I,Q plane. Summation at node244 cancels signal vectors rotating clockwise in I,Q plane 286 andreinforces those rotating counterclockwise in the I,Q plane. The upper-and lower-sideband signals are used by the supervisor program duringsystem start-up to search for the photodetector frequency and to set thelocal oscillator frequency for the baseband demodulator.

The I,Q complex signal is also applied to the pipeline process in ablock 316 (see FIG. 32), which detects and segments signals fromindividual objects in the flow stream, tests these signals againstpredetermined acceptance criteria, and computes the object velocity fromthe accepted signals. The details of the steps implemented in block 316are shown in FIG. 34.

The signal threshold concept illustrated in graph 302 (FIG. 31A) is usedto segment the signal stream into sample packets, each of whichrepresents an object or an aggregate of objects in the flow stream. Themost accurate velocity measurements are those derived from the signalsfrom isolated single objects. Signals from aggregate objects, i.e.,signals from multiple objects coexisting in the FOV of the velocitydetector, carry phase errors caused by the interference among thesignals from the individual objects. The segmentation then accepts onlythose signals with an envelope width close to that predicted for asingle object passing through the FOV at the current expected velocity.Because the envelope width is inversely proportional to velocity, thesupervisor program tracks the known velocity and corrects the widthlimits as the velocity changes.

Referring to FIG. 34, each base pair of the I,Q complex signal isanalyzed starting in a block 334. Segmentation of a packet begins whenthe magnitude of the signal crosses a threshold 371 while rising,detected in an step 336, and ends with the magnitude falls back acrossthreshold 371, detected in a step 342. The sample count, n, and theunwrapped phase, Φ_(m)(n), are set to zero in a step 338 when the risingedge of the packet is detected. The unwrapped phase is computed in astep 344 for each sample following the rising edge, and the sample countis incremented each time a new sample is acquired in a step 340. Oncethe falling edge of the packet is detected, the sample count, n, istaken as a width 364 of the packet. The phase samples Φ_(m)(0) throughΦ_(m)(n) are used in the computation of the average frequency of thesignal packet, and, subsequently, the velocity of the object.

However, each packet must meet two criteria before being accepted as auseful signal. First, the packet width is compared to upper and lowerwidth limits 366 in a step 346. The packet is rejected if the widthfalls outside those limits. The radial fractional frequency, ω(n), iscomputed for each sample within the packet in a step 348. The unwrappingalgorithm cannot deliver values outside of the range from −π to π, sincethe Nyquist limits are −π and π radians/sample. Division by π inoperation 348 expresses ω(n) as the dimensionless fraction of theNyquist limit. The variance is computed for the ensemble of values ω(1)through ω(n) in a step 350 and compared with a maximum limit 373 in astep 360. This limit is a constant determined empirically for deliveryof the required accuracy in the velocity measurements while limiting thenumber of rejected objects.

If the wave packet is accepted as representing a single object and ashaving an acceptably low frequency variance, an object velocity 368 iscomputed in a step 362 as follows:${\overset{\_}{f}}_{bb} = {\frac{\sum\limits_{i = 1}^{n}\quad {\omega (i)}}{n} \cdot f_{Nyq}}$

and

ν_(o)(mm/sec)=({overscore (ƒ)}_(bb)+ƒ_(LO))·s

where:

ω(i)=fractional frequency for sample i

ƒ_(Nyq)=Nyquist frequency for baseband

{overscore (ƒ)}_(bb)=mean baseband frequency (Hz)

ƒ_(LO)=local oscillator frequency (Hz)

s=grating pitch (mm)

ν_(o)=particle velocity (mm/sec).

In FIG. 35A, a graph 370 represents a plot of the baseband frequencyversus time for the magnitude signal shown in graph 302 (FIG. 31A), butwith only the qualified signals retained. A graph 372 in FIG. 35B showsthe series of object velocities computed in operation 362 (see FIG. 34)from the baseband frequency data shown in graph 370.

For each accepted object, a velocity, ν_(o), is delivered to a scrollingobject velocity list 318 of FIG. 32. Every time a new velocity value isadded to the scrolling list, the oldest value is removed from the list.A running average computation in a step 320 constantly determines arunning average 324 of the values in the scrolling velocity list at arepetition rate determined by the supervisor program.

FIG. 36 shows the structure of the supervisor program for the thirdembodiment of the present invention. Operation of the velocity detectionsystem is initiated with the instrument calibration in a step 374, inwhich the noise from the photodetector channel is determined andanalyzed in the absence of an optical signal. This step can beaccomplished by turning off the light sources in the system or stoppingthe flow of objects through the flow tube. The purpose of the noisemeasurement is to establish a reference against which theinformation-bearing signal will be compared for setting a threshold foraccepting or rejecting phase samples.

The calibration operation measures the noise level at a plurality ofamplifier gain settings and stores these measurements in a table 376 ofnoise level vs. gain. Table 376 will be used to select an initial gainsetting for variable gain amplifier 52. As the amplifier gain is variedto regulate the signal strength during normal operation, the correctnoise level for setting the decision threshold will be read from table376 and applied to a threshold calculation in a step 378. Once thecalibration operation has been completed, the light source or sourcesare turned on, and objects are introduced into the flow stream for imageacquisition, as shown in a step 380.

The next task of the supervisor program shown in a step 382, is tosearch the spectrum for the photodetector signal. In the absence of anya priori knowledge of the flow speed, this initial search must span theentire range of frequencies in the spectrum, and may entail sweeping thespectrum a number of times until a strong signal is found. Step 382sweeps the frequency of the local oscillator and captures a short timesegment of upper sideband 330 and lower sideband 332 signals (see FIG.33). As the local oscillator is swept across the actual frequency of thephotodetector signal, the lower sideband amplitude will increase andthen drop. Then the upper sideband amplitude will increase and thendrop. For the broad sweep to locate the approximate photodetectorfrequency, the local oscillator is varied in large increments to speedthe search, and the search in step 382 measures the root mean square(rms) sum of the sidebands as follows: $\begin{matrix}{P_{usb} = {\sum\limits_{i = 1}^{N}{U^{2}\lbrack i\rbrack}}} \\{P_{lsb} = {\sum\limits_{i = 1}^{N}{L^{2}\lbrack i\rbrack}}} \\{P_{sum} = \sqrt{P_{usb}^{2} + P_{lsb}^{2}}}\end{matrix}$

where:

U[i]=upper sideband amplitude of ith sample

L[i]=lower sideband amplitude of ith sample

N=number of time samples in tested signal segment

P_(sum)=integrated sideband power for signal segment.

FIG. 37 is a graph 406 of the integrated sideband power versus the localoscillator frequency for the broad search sweep. The width of a powerenvelope 408 is two times the bandwidth of lowpass filters 230 and 232(see FIG. 25) in the baseband demodulator. The desired local oscillatorfrequency is located at a dip 410 between the two peaks in the powerenvelope. However, this frequency is poorly resolved because of thelarge steps used in search sweep 382.

Referring back to FIG. 36, a more accurate estimate of the desired localoscillator frequency is made in a step 384 by varying the localoscillator frequency over a narrow range covering the power envelope.This narrow sweep is illustrated by a graph 414 in FIG. 38, which is anoverlay of an upper sideband power 416, called P_(usb), and a lowersideband power 418, called P_(lsb), as a function of local oscillatorfrequency. The search in step 384 of FIG. 36 finds a frequency 420 inFIG. 38 at which the upper sideband and lower sideband are of equalpower. As will be evident in FIG. 38, this frequency is approximately2500 Hz. Under this condition, the local oscillator frequency isapproximately equal to the photodetector signal center frequency, andthe baseband demodulation system can be used to measure the exactphotodetector signal frequency.

With the magnitude threshold and the local oscillator frequency set,object processing can commence. During object processing, the supervisorprogram continuously monitors the sideband signals in a step 388 (FIG.36) using sample locations accepted by signal processing step 316 (seeFIG. 32). The selected sideband samples are used to monitor the balancebetween the power in the upper sideband signal and that in the lowersideband signal. Imbalance between the two sideband signals indicatesthat the photodetector frequency has shifted and that the localoscillator frequency should be adjusted. The sideband balance will berepeatedly computed in a step 388, the balance values stored in a table390, adjustments to the local oscillator will be computed in a step 394,and applied in a step 386. The number of values maintained in table 390can be modified to adjust the response time and stability of the localoscillator feedback loop.

The sideband signal levels are checked to determine if they have beenlost in a step 392. If both the upper sideband and lower sidebandsignals are lost, the supervisor program interrupts signal processingand returns to search routine 382 to tune the system back to thephotodetector signal frequency, if possible. The supervisor program willremain in the search mode until a signal is acquired or the velocitydetection system is turned off.

The gain of the variable gain amplifier is regulated during systemoperation as well, in order to optimize the SNR of the velocity detectoras specimen characteristics change. The amplifier gain regulation systemof the supervisor program implemented in a step 396 creates thehistogram of the peak magnitudes of the accepted signal packets. A step398 provides for counting a number of samples occupying a pre-determinednumber of levels near the top of the analog-to-digital converter outputscale, and maintains a list of the most recent count results in a table400. That table is analyzed to determine a gain adjustment in a step402. The gain adjustment implemented in step 402 will use a “fastattack,” “slow recovery” algorithm, as described above, to regulate thegain of the variable gain amplifier.

The new gain setting is set in a step 404 and is provided to a noisecalibration table 376. The response time and stability of the gaincontrol feedback loop can be modified by adjusting the number of samplesmaintained in table 400 and analyzed in step 402.

TDVM of Objects Using Paired Nonuniform Optical Gratings

In the fourth preferred embodiment of the present invention, the lightcollected for velocity measurement is modulated by two optical gratingsand sensed by two photodetectors, as shown in FIG. 43. The velocity ismeasured by cross-correlating the signal from the first photodetectorwith that from the second photodetector, yielding a time-of-flight valuethat is converted into a velocity of the object.

The cross-correlation of two signals is carried out by convolving thetwo signals and extracting information from the output of theconvolution operation, which is called the correlogram. The convolutionin the time domain is defined by the following equation:f₁(t) * f₂(t) = ∫_(−∞)^(∞)f₁(λ)  f₂(t − λ)λ.

The value of the convolution for every time sample, t, is the sum overinfinity of the product of the two functions, but with the secondfunction offset by time t. The utility of the convolution operator liesin the fact that it is equivalent to multiplication in the frequencydomain:

Given the general notation:

F(e ^(jω))=the Fourier Transform of ƒ(t)

if

ƒ₃(t)=ƒ₁(t)*ƒ₂(t)

then

F ₃(e ^(jω))=F ₁(e ^(jω))·F ₂(e ^(jω)).

A filter with a desired frequency response H(ejω) can be implemented asa time-domain operation, for example, by applying its inverse FourierTransform, h(t), in the convolution integral. In the present invention,however, the utility of the convolution operator is in the measurementof the time delay between two signals. In the simplest case, the twosignals are identical to one another, except that the second signal isdelayed by time t0. As shown in the following equations, applying timedelay to a signal is equivalent to convolving that signal by the delayedimpulse function, δ(t−t0):

ƒ₂(t)=ƒ₁(t−t ₀)

then

ƒ₂(t)=ƒ₁(t)*δ(t−t ₀).

Because convolution is associative, the problem of convolving the firstsignal ƒ₁(t) with the second signal, ƒ₂(t), can be solved by convolvingƒ₁(t) with itself and time delaying the result. Thus,

ƒ₁(t)*ƒ₂(t)=ƒ₁(t)*ƒ₁(t)*δ(t−t ₀)

if

ƒ₃(t)=ƒ₁(t)*ƒ₁(t)

then

ƒ₁(t)*ƒ₂(t)=ƒ₃(t−t ₀).

Conversely, it is possible to measure the time delay between two signalsby convolving one with the other and detecting the amount of time delayin the result.

FIG. 39 illustrates the convolution of two similar signals 422 and 424,which are the inputs of a convolution operator 426 arriving at differenttimes. A correlogram 428 is a plot of the amplitude of the integratedproduct of signals 422 and 424. The horizontal axis of the correlogramrepresents the time delay applied to signal 422 relative to signal 424using the convolution operation, scaled in units of time samples. Adelay of around 400 samples is required to align signal 422 with signal424, which is evident from the peak value of the correlogram amplitude.Note that the correlogram is broader than either of the two signals.This condition can be understood from the recognition that convolving asignal with itself is equivalent to squaring the spectrum of the signal,a step that compresses the spectral distribution. Narrowing thebandwidth of a signal broadens the signal in the time domain.

In the velocity detection system, the signals 422 and 424 might havebeen generated by two objects traversing an optical grating 432, asshown in FIG. 40. A graph 430 represents the Gaussian illuminationprofile applied to the field of optical grating 432. An objecttraversing the illumination profile will generate a signal with aGaussian envelope and an oscillation frequency directly proportional tothe velocity of the object and inversely proportional to the opticalgrating pitch.

Detecting the peak of correlogram 428 might be accomplished using asimple peak detector. The correlogram is broad, however, and the peak ofthe envelope might not coincide with the peak of an oscillation cycle.More elaborate detection schemes might improve the accuracy, but it isalso useful to generate a narrower correlogram with a more clearlydefined peak. This objective is accomplished by using an optical grating436 shown in FIG. 41. Optical grating 436 has a nonuniform pitch, withline width (opaque and transparent bar width) decreasing linearly fromthe left end of the optical grating to the right end of the opticalgrating. The optical grating is aligned with a beam profile 434. Graphs438 and 440 in FIG. 42 represent signals that could be generated byphotodetectors in response to the light from two objects passing throughoptical grating 436. A resulting correlogram 442 is more compact thancorrelogram 428, suggesting that the time delay value might be extractedmore easily from correlogram 442 than from correlogram 428.

The preferred embodiment of the correlation-based signal generationsystem uses a detection system that takes advantage of the nonuniformoptical grating pitch. FIG. 43 illustrates the components used in thepreferred embodiment. Light source 12 and lens 36 illuminate the FOV offlow tube 16 for the purpose of velocity measurement. The optical systemcomprising lenses 40,44, and 78 and beam splitter 76 form images of theobjects passing through the FOVs on two optical gratings 444 and 446having nonuniform but identical patterns of opaque and transparent bars.

As shown in FIG. 44, images 450 and 452 of optical gratings 444 and 446are aligned end-to-end along the axis of flow. The boundary between thetwo images is aligned with the midpoint of an illumination profile 454.Light scattered or emitted by objects in the flow stream is modulated byoptical gratings 444 and 446 and the modulated light is delivered tophotodetectors 50 and 50 a by lens 48 and a lens 448, respectively (seeFIG. 43).

FIG. 45 illustrates the performance of the correlation operation forsignals generated using the optical grating geometry shown in FIG. 44. Asignal 456, produced in response to light modulated by optical grating444 at the upstream side of the illumination field, grows in amplitudeand increases in frequency with time, and terminates when the objectmoves into the field of downstream side of the illumination field. Asignal 458, produced in response to light modulated by optical grating446 at the downstream side of the illumination field, starts at highamplitude and low frequency. The amplitude decays with time as thefrequency increases. A correlogram 460 shows a very distinct peak at theexact delay value that brings the two signals into alignment.

FIG. 46 shows an expanded view 462 of correlogram 460. For this view,the delay limits of the cross-correlation operation 426 were expanded toshow that as the delay of signal 458 approaches the delay for optimalalignment, it first passes through a region 466 in which the correlogramappears very noisy. This region is where the high-amplitude part ofsignal 456 is aligned with the high-amplitude part of signal 458.However, the particular optical grating configuration shown in FIG. 44provides the benefit that a primary peak 464 of correlogram 460 isbordered on both sides by very low-level signals 468. The noisy regionof the expanded correlogram is avoided by using only those delay valuesclose to the actual time of flight of the objects from a location on theupstream grating to the corresponding location on the downstreamgrating. A feedback loop in the supervisor program is used to regulatethe convolution time delay limits to maintain this condition.

FIG. 47 shows the functional processing blocks used for the signalacquisition and processing for this embodiment of the present invention.The signals from photodetectors 50 and 50 a are applied to variable-gainamplifiers 52 and 52 a, respectively. The outputs of the amplifiers areapplied to bandpass filters 54 and 54 a to eliminate DC bias and toprevent aliasing when the signals are sampled by ADCs 56 and 56 a. Thedigital outputs of the ADCs are delivered to a signal processing stage470, which accepts a signal segment of a predetermined length anddelivers an estimate of the object velocity to a scrolling velocity list472, if acceptable signals from objects traversing the flow cell arepresent in that segment. If a new velocity value is delivered by thesignal processing operation, it is added to list 472 and the oldestvalue on the list is deleted. A step 474 delivers the average of thevelocity values in list 472 at the rate at which signal segments arecaptured in the signal processor to facilitate computation of a runningaverage 476.

FIG. 48 shows the detailed architecture of the signal processingoperation. For every cycle interval of the signal processor, concurrentsegments of the digitized signals from photodetectors 50 and 50 a arecaptured in steps 478 a and 478 b. The captured segments aresimultaneously applied to magnitude calculators 480 a and 480 b, and toa step 482, which provides for determining a cross-correlation. Eachmagnitude calculator uses the following algorithm for computing thesignal level:$M_{j} = {\sum\limits_{i = 1}^{N}{{A\lbrack i\rbrack}}}$

where:

N=length of the signal segment

A[i]=value of the ith sample of the segment

M_(j)=magnitude of the jth signal segment.

The magnitude values are sent to supervisor program 486 to be used toregulate the photodetector amplifier gain.

The convolution (or cross-correlation) carried out in step 482 generatesthe correlogram using the following algorithm:

for (k=Min_Delay; k<=Max_Delay; k++)

{ m = k − Min_Delay; for (i = 0; i <= Correlation_Length; i++ { j = k +i; C[m] += Signal 1[i]* Signal 2[j]; } }

FIG. 49 illustrates the results of the correlation algorithm. A signalsegment 496 from the first photodetector is convolved with a signalsegment 498 from the second photodetector through a series ofmultiply-and-accumulate operations to generate a correlogram 508. Foreach value in correlogram 508, the first P samples, where P=CorrelationLength, of signal 496 are multiplied by the corresponding samples ofsignal 498 from sample Q, where Q=Delay, to sample R, whereR=Delay+Correlation Length. The products of the sample-by-samplemultiplication are summed to produce the values of the correlogram. TheDelay value begins at a Min_Delay 504 and advances one sample for everysample in correlogram 508 until it reaches a Max_Delay 506.

The location of the peak of the correlogram is found in a step 484 (seeFIG. 48) using a simple peak-detection algorithm, as follows:

C_(pk)=0

for (d=1d=N)

if (C[d]>C _(pk))

then

[(C _(pk) =C[d]) and (N_(d)=d)]

where:

C[d]=value of correlogram at delay d

N_(d)=location of correlogram peak

C_(pk)=peak amplitude of correlogram.

In a step 488, the peak amplitude of the correlogram is compared to athreshold. This threshold is a fixed value accessible to a supervisorprogram 486. Regulation of the photodetector signal level usingvariable-gain amplifiers 52 and 52 a enable the use of a fixedthreshold.

If the peak amplitude of the correlogram exceeds the threshold, the peaklocation from step 484 is accepted and passed to a step 490 in which thevelocity is calculated. The velocity calculated in step 490 thenreplaces the oldest velocity value in scrolling velocity list 472 (seeFIG. 47). If the amplitude of the correlogram is less than or equal tothe threshold, the signal processor returns a NULL value 494, andscrolling velocity list 472 remains unchanged.

For a valid correlogram, a velocity 492 is computed from the correlogrampeak location using the following relation:

t _(t) =N _(d) ·t _(samp)

and

ν=s/t _(t)

where:

t_(t)=transit time, grating-to-grating (sec)

t_(samp)=signal sample time

s=grating-to-grating distance (mm)

ν=velocity (mm/sec).

The running average velocity estimate is acquired by the supervisorprogram and translated into a frequency signal used by Instrument TimingGenerator 146, as shown in FIG. 19.

FIG. 50 shows the structure of the supervisor program for the fourthembodiment of the present invention. System operation is initiated in astart block 510 when the gain of the variable gain amplifier is set tonominal values, and objects are introduced into the flow stream forimage acquisition.

In a step 512, the supervisor program performs a cross-correlationbetween segments of the two photodetector signals using a wide span ofcorrelation delays. The delay value yielding the largest peak in thecorrelogram is used in a step 514 to compute the initial velocity. Thecross-correlation delay limits are set in a step 516 to bracket thisinitial delay value.

With the cross-correlation delay limits set, object velocity processingcommences. During this processing, the supervisor program continuouslymeasures the velocity using the cross correlation method and adjusts thecorrelation delay limits to maintain execution of a short-spancross-correlation in the neighborhood of the delay required for thecurrent flow velocity. Use of the short-span cross correlation reducescomputation time.

A step 478 a provides for capturing the photodetector signal segments; astep 482, computes the short-delay-span cross-correlation; a step 490computes the velocity and tabulates the results in a scrolling velocitylist 472 to provide the information for adjusting the correlationlimits. The limits are determined in a step 518 from the average of thevalues in velocity table 472. This average velocity is converted to anexpected correlation time delay value, and the limits are placedsymmetrically around this expected delay. The offset from the expectedvalue to the minimum delay and the offset from the expected value to themaximum delay are empirically determined and stored in a look-up tableto be used in the limit calculation step 518. In a step 516, thecorrelation offset limits are set and stored in locations accessible tothe cross-correlation determination in step 482 for use in processingthe next segment of the photodetector signals.

The supervisor program also continuously optimizes the gain of thevariable gain amplifiers to maximize SNR as specimen characteristicschange, without causing saturation at the ADC. The process of regulatingthe gains of the amplifiers is initiated in steps 480 a and 480 b, whichcompute the integrated magnitudes of the signal segments. The magnitudesare delivered to tables 520 and 526, which contain a set of the mostrecent magnitudes. Adjustments to the gains are computed by steps 522and 528 from the maximum magnitudes in tables 520 and 526 and the newsetting to variable gain amplifiers 52 and 52 a are made in steps 524and 530. The gain adjustment in steps 524 and 530 uses a “fast attack,”“slow recovery” algorithm as described above, to regulate the gain ofthe variable gain amplifiers.

It should be noted that the method described above for processing theelectrical signals produced by the photodetector(s) in the third andfourth embodiments using the TDVMs can also be applied to determiningthe velocity of objects disposed on a substrate (and the velocity of thesubstrate) that is caused to move through the FOV. Generally, either theTDVM or the FDVM approach can be used for determining the velocity ofany configuration of objects moving through the FOV.

The Use of Calibration Beads in Flow Imaging Systems

The above descriptions of the velocity detection apparatus and methodsfor detecting velocity of objects in flow have assumed that the objectupon which the velocity measurement is performed is representative ofthe objects of primary interest to be imaged. Under certain conditions,it is beneficial to introduce into, or spike, the sample containing theparticles of imaging interest with different types of particles,specifically to facilitate velocity measurement.

In the description below, and the claims that follow, particlesintroduced into a flow of fluid in an imaging apparatus for the purposeof establishing a velocity are referred to as “calibration beads”. Inthe examples presented herein such calibration beads are preferablypolymer micro spheres, but it should be understood that substantiallyany object capable of being suspended in a fluid, and whose dimensionsare compatible with the imaging system being employed, can be utilizedas a calibration bead. With respect to the dimensions, such calibrationbeads must be small enough to pass through the flow cell of the imagingsystem without obstruction, and yet large enough to be readilydetectable by the imaging systems optics and sensors. An optimal sizecalibration bead for a first imaging system may not represent an optimalsize for a second imaging system. Examples of particles that can bebeneficially employed as calibration beads include cells, cell clusters,labeled and unlabelled micro spheres (polymer, copolymer, tetra polymerand silica beads).

As described in detail above, flow imaging instruments employing TDIdetectors require accurate velocity information for the objectsentrained in the flow. Calibration beads can facilitate such velocitymeasurements. The use of such calibration beads is particularly helpfulwhen the fluid flow contains only a small number of objects, when only asmall volume of sample fluid is available, when only limited amounts oflight from target cells are available, when a distribution of targetcells in a sample is uneven, and to facilitate diagnostic andcalibration procedures. Such uses are described in more detail below.

Small Cell Concentration: It is preferable to continuously monitor thespeed of the objects in the flow cell, allowing for continuouslysynchronized TDI detector with flow speed. Samples having lowconcentration of objects, such as cells, present difficulties withcontinuous TDI detector/flow speed synchronization, because there isgenerally a relatively large time duration between different cellspassing through the field of view. Adding a high concentration ofcalibration beads enables the continuous detection of flow speedvelocity, thereby facilitating substantially continuous TDIdetector/flow speed synchronization.

As noted above, the specifications of an imaging system will enable apreferred range of dimensions to be determined for calibration beads.Similarly, the specifications of an imaging system will also helpdetermine a preferred concentration of calibration beads, the preferredconcentration being selected to ensure that sufficient calibration beadsare provided so that the velocity of the fluid in the flow cell iscontinually monitored. Useful specifications, as will be described inmore detail below, include information about the TDI detector and theflow cell.

With respect to flow cells, it should be recognized that flow cellsemployed in flow imaging systems have a sheath diameter, representingthe diameter of the fluid path in the flow cell. Preferably, thatdiameter is sufficiently large to allow particulate laden fluids to passthrough the flow cell without blockage. A sheath fluid, which does notinclude the objects of interest (or the calibration beads) is introducedinto the flow cell. A core fluid, containing the objects of interest aswell as any calibration beads, is also introduced into the flow cell,such that the core fluid generally passes through the flow cell along acentral axis, and such that the sheath fluid generally surrounds thecore fluid. The focal point of the imaging system is aligned with thecore fluid, so that objects of interest can be imaged.

Understanding the definitions of specific terms employed in thefollowing description will be helpful. In the context of the presentinvention, the terms object space and image space are employed todifferentiate between the actual object (object space) and the projectedimage of the object onto the TDI camera (image space). A preferred flowimaging system employs a 36× optical magnification and a TDI camerawhose pixels are 18 microns square. Therefore, in the image space, thesystem's resolution, as determined by the pixilated camera, is 18microns. This resolution (given a 36× magnification) allows for aresolution in object space of 0.5 microns; therefore, cellular imageswill have 0.5 micron resolution. Note that in certain applications, itmay be desirable to increase the magnification (for example, using thesame TDI camera, 72× magnification would result in a resolution of 0.25microns in object space). It should be understood that as magnificationis increased, the field of view is decreased; thus the need for enhancedmagnification must be balanced with a desired field of view.

The phrase “pixel dimension in object space” indicates that the 0.5 or0.25 micron dimension of the object (depending on the magnification)coincides to a single camera pixel (18 microns in image space). By usingbeads with sizes of 500 to 200 nanometers, the image of such beadsshould be confined to a single camera pixel. However, anydistortion/aberrations with the optical path will contribute todispersion and defocusing of light, causing light to fall on more than asingle camera pixel in object space. This spreading of the light isreferred to as the point spread function.

An update cycle refers to the velocity feedback loop of the TDI cameraemployed in a preferred flow imaging system. As discussed in detailabove with respect to the methods for determining velocity, it isdesirable to determine accurate velocity information to enable the TDIcamera to be synchronized to the flow of fluid. Assuming that apreferred TDI camera has a feedback loop of 5 Hz, and assuming a flow of2 nanoliters of sample flowing per second, a preferred sample flow(during each 5 Hz cycle) is 0.4 nanoliters (2 nanoliters/sec divided by5 Hz).

For a particular imaging system, assume a TDI camera of the imagingsystem has a maximum line read out rate frequency of 50 kHz, and a pixelheight in image space of 0.5 microns. Such a system yields a maximumflow speed of 25 mm per second. Further assume that the diameter of thecore fluid flowing through the flow cell of the imaging system is 10microns in diameter, yielding a flow rate of approximately 2 nanolitersper second. Given that the velocity/TDI camera feedback loop bandwidthis approximately 5 Hz, this allows approximately 0.4 nanoliters of fluidto pass per update cycle, as described above. In a preferred embodiment,an optimal situation would be to provide one particle in flow cell perupdate cycle, that volume, which corresponds to 1 particle per 0.4nanoliters of fluid. This in turn corresponds to a preferred calibrationbead concentration of 2.5×10⁶ particles/ml, for the described imagingsystem. Of course, performing the above calculations for imaging systemshaving different specifications would result in a different preferredconcentration.

Often the concentration of objects of interest in a sample will be farbelow the preferred concentration calculated above. Sufficientcalibration beads can be employed such that a preferred concentration ofobjects that can be used to determine velocity are present in the flowcell during the use of the imaging instrument.

Small Sample Volume: Another circumstance in which the use ofcalibration beads can facilitate accurate velocity measurements is whenthe sample volume is small. Certain samples are limited in theiravailable volumes. Therefore, it is advantageous to design analyticaltechniques that use the least amount of sample as possible. It should beunderstood that it often takes a significant volume of sample toinitialize a system, i.e. dead volume, and to establish a stablehydrodynamically focused core with respect to the core/sheath structurein a hydrodynamically focused flow cell. Particularly when the volume ofsample is limited, it is preferred to employ a substitute core/samplefluid during this initialization process. Rather than using the sampleas a core fluid during such initialization, a fluid including onlycalibration beads (and no sample) is preferably employed until thesystem is stabilized. The calibration beads in such a calibration corefluid will enable the TDI Camera/Velocity synchronization to beestablished. When the hydrodynamically focused core stream is stable andthe system is initialized, the sample containing the objects of imaginginterest can be introduced into the core stream for analysis of thesample objects. Further description of the fluidic system and modes ofoperations can be found in the section entitled “Description of aPreferred Fluidic System”.

Limited Light from Certain Cells: Yet another situation in which the useof calibration beads can facilitate the establishment of TDICamera/Velocity synchronization is when the amount of light from anobject of interest is relatively small, or at least smaller than anamount of light that would be provided by a calibration bead. Thevelocity detection measurement described in detail above employs anoptical grating to modulate light from cells or other objects. As notedearlier, this modulated light can be a result of scattered light fromthe object, a stimulated emission from the object, an un-stimulatedemission from the object, or light transmitted through the object. Thesignal strength of the light from the objects can be proportional thesize of the object. In the case where velocity detection is determinedby light scatter from the object, a larger light intensity allows for anincreased signal to noise ratio, enabling increased precision in thevelocity measurement. Particles of interest which are quite small (i.e.submicron in size) will scatter relatively small amounts of light,resulting in a reduced velocity detection resolution. By usingrelatively large calibration beads (preferably one to ten microns indiameter), one can increase light scatter and therefore increasevelocity detection resolution.

Non-Uniform Cell Scatter: Still another situation in which the use ofcalibration beads can facilitate the establishment of TDICamera/Velocity synchronization is when the imaging system will be usedwith a variety of different samples or with a single sample thatincludes a plurality of different objects of interest, each of whichproduces a different image. Use of calibration beads will allow for amore uniform and precise velocity detection, particularly if themajority of the objects passing through the flow cell are calibrationbeads. Given that imaging systems such as those described in detailabove can be used for a vast array of different samples, having aconsistent particle (i.e. calibration beads) to perform velocitydetection allows for consistency in the operation of the assays.

Optical Self-Diagnostics, Calibration and Quality Metrics: The use ofcalibration beads in a flow imaging instrument provides a known datasource that can be employed in various self-diagnostic, calibration andquality metric applications for the optical system of the instrument.Imagery collected from calibration beads can be used to determine pointspread functions associated with an imaging system, to determine asensitivity of an imaging system, and to determine a focal point of theimaging system. Each of these applications is described in additionaldetail below.

Point Spread Function: By using the imagery collected from smallcalibration beads (bead sizes equal to or smaller than the pixeldimension in object space) one can determine the point spread functionby comparing the known calibration bead size with the image. As thepixel size in an anticipated embodiment of an imaging system might be0.5 or 0.25 microns in objects space, this would require utilizingcalibration beads of 200 to 500 nm in size. However, such small beads donot provide much scattered light, and it would be preferable to employlarger calibration beads to provide a greater velocity detectionresolution. To achieve such enhanced velocity detection resolution itwould be preferable to employ calibration beads having a size of fromabout 1-10 microns.

One solution that would provide both an acceptable velocity detectionresolution and facilitate the determination of the point spread functiondescribed above is to provide a set of calibration beads that includes aknown distribution or grouping of different sized calibration beads, inorder to achieve both the goal of determining point spread function andproviding the desired velocity detection resolution.

With respect to determining the point spread function, it isadvantageous to use fluorescent labeled beads in such a set, as theimagery collected in the fluorescent image channels will not bedistorted by refractive index differences between the bead and corematerial, which does affect both the bright field and dark field channelimagery data. Therefore, in addition to having a distribution ofdifferent sized calibration beads, it is advantageous to have pluralityof different types of calibration beads (i.e. differently labeledcalibration beads) within the calibration bead set.

Sensitivity Calibration: By utilizing a calibration bead set with somedistribution of calibration beads having known fluorescent labelconcentrations, the measured fluorescent image can be compared to theknown MESF (molecules of equivalent soluble fluorochrome) to allow forcalibration, to insure that the imaging system is providing reproducibleresults.

Focus: By comparing the size and shape of the collected images againstknown images of the calibration beads, one can determine if the particleis in focus and diagnose the overall performance of the imaging optics.

Flow Cell Self-Diagnostics, Calibration and Quality Metrics: The knowndata source provided by the calibration beads can not only be used forself-diagnostic, calibration and quality metric applications for theoptical system of the imagining system, but also for the flow cell ofthe imaging system. Imagery collected from calibration beads can be usedto determine core size and stability and TDI/flow speed synchronization.Again, each of these applications is described in additional detailbelow.

Core Size and Stability: Using known and highly concentrated calibrationbeads one can image these beads to determine if the hydrodynamicallyfocused core is stable, as well as determine the core size.Specifically, a histogram of the lateral position of the imaged beadswill produce a normal distribution defining the core diameter. The useof calibration beads in a relatively high concentration (such that atleast one calibration bead is imaged during each update cycle, as wascalculated above) enables continuous monitoring of the core size.

System Check for Camera/Flow Speed Synchronization: By comparing thecollected images of calibration beads to the known circular crosssection of the calibration bead, one can determine if the flow speed issynchronized to the TDI camera. Collected images that are elongated inheight, or shortened in height, are indicative of poor synchronization.

Preferred Materials and Properties of Calibration Beads: The calibrationbeads are preferably polymeric beads of any of the following types:polystyrene, styrene/divinylbenzene copolymer (S/DVB),polymethylmethacrylate (PMMA), polyvinyltoluene (PVT), styrene/butadiene(S/B), styrene/vinyltoluene (S/VT). Mixtures of different types ofcalibration beads may be used. Preferable calibration beads will havedensities in the range of 0.9-2.3 grams per cubic centimeter, anddiameters that range from 20 nanometers to 50 microns.

Calibration beads may incorporate surface functional groups enabling thecovalent coupling of ligands. Such surface functional groups preferablyinclude: sulfate based groups (—SO₄), aldehyde based groups (—CHO),aliphatic amine based groups (—CH₂—NH₂), amide based groups (—CONH₂),aromatic amine based groups (—NH₂), carboxylic acid based groups(—COOH), chloromethyl based groups (—CH₂—Cl), hydrazide based groups(CONH—NH₂), hydroxyl based groups (—OH), and sulfonate based groups(—SO₃).

Calibration beads can be beneficially incorporate a coating of protein Aor streptavidin. Further, calibration beads can include dyedmicrospheres of different colors, fluorescent labeled microspheres,magnetic microspheres, and molecularly imprinted micro spheres.

It should be understood that while the above noted materials andproperties are preferred, such materials and properties are merelyexemplary, and should not be considered to limit the invention.Calibration beads of other materials and properties are anticipated, solong as such materials and properties combine to achieve calibrationbeads that 1) can be entrained in a fluid; 2) are of a size that issufficiently small so as to be able to readily pass through the flowcell of an imaging system without obstruction; and 3) are of a size thatis sufficiently large so as to facilitate the desired velocity detectionresolution in a particular imaging system.

FIG. 51 illustrates continuously segmented multispectral image data setof a population of fluorescent calibration polymer beads generated bythe ImageStream™ prototype system. The bead population consisted of 4micron diameter unlabelled beads, 4 micron FITC(fluorescein-isothiocyanate) labeled beads, and 4 micron PE(phycoerythrin) labeled beads. The system is configured for brightfieldimagery (600-650 nm), PE fluorescence imagery, FITC fluorescence imageryand dark field imagery (488 nm). Images of each bead appear in thebright field and dark field channels, along with a fluorescence image inthe channel corresponding to the dye present on each of the 4 micronfluorescent calibration beads. The imagery was gathered at amagnification of 20×, corresponding to the pixel size of approximately0.65 microns at the object. The dark field imagery shows the lensingeffect of each bead due to its large index of refraction relative to thebuffer solution.

FIG. 52 illustrates continuously segmented data from ten unlabeledbeads, each 350 nm in diameter. The orthogonal scattered light imageswere collected using 488, 532, 670 and 2780 nm laser excitation. Thisdata was collected with an early prototype imaging system employingchromatically uncorrected optics optimized at 670 nm. As a result, the670 nm scattered light data is most in focus, and in some cases thescattered light is represented by a single or few pixels of thedetector. These 350 nm calibration particles can be used to determinethe point spread function.

FIG. 53 illustrates the fluidics of a preferred imaging system. Thesystem has three fluidic pumps. The first pump is a sample syringe pump600, which holds a ten microliter glass syringe. A preferred syringepump is disclosed in a commonly assigned copending U.S. Provisionalpatent application entitled “CELL SUSPENSION ROTATING FLUIDIC PUMP”,which is herein specifically incorporated by reference. Sample syringepump 600 allows rotation of the cylindrical barrel along its barrelaxis, in order to uniformly suspend particulates contained in the sample(e.g. beads, cells, or other objects of interest), as well as amotorized control of the piston plunger for low pulsatility and lowvolume injection capabilities. Syringe pump 600 aspirates or loadsfluidic sample from a sample holding pipette 674. Prior to loading thesample into syringe pump 600, a sample valve controller stepper motor620 actuates a sample valve 660 to connect the 25 micron diameterfluidic line between sample syringe pump 600 and sample holding pipette674.

The second pump is a bead syringe pump 602, which holds a ten microliter glass syringe. Preferably, the calibration beads discussed aboveare introduced into a flow imaging system via bead syringe pump 602. Apreferred embodiment of syringe pump 602 is similarly disclosed in theabove noted copending U.S. Provisional patent application, and allowsfor rotation of the cylindrical barrel along its barrel axis in order touniformly suspend particulates contained in the sample (e.g. beads,cells, or other objects of interest) as well as a motorized control ofthe piston plunger for low pulsatility and low volume injectioncapabilities. Syringe pump 602 aspirates or loads fluidic suspension ofbeads from a bead holding pipette 676. Prior to loading the beadsuspension into bead syringe pump 602, a sample valve controller steppermotor 621 actuates a bead valve 661 to connect the 25 micron diameterfluidic line between bead syringe pump 602 and bead holding pipette 676.

The third pump is a sheath syringe pump 604, which holds a 20 milliliterglass syringe. Again, a preferred sheath syringe pump 604 is disclosedin the above noted copending U.S. provisional patent application, allowsfor motorized control of the piston plunger for low pulsatility and lowvolume injection capabilities. Sheath syringe pump 604 aspirates orloads sheath fluid from a sheath reservoir 651. This reservoir is ventedto ambient atmosphere using a vent line 636 and a 0.2 micron filter 680.Prior to loading the sheath fluid into sheath syringe pump 604 a sheathvalve controller stepper motor 622 actuates a sheath valve 662 toconnect a 50 micron diameter fluidic line 635 between sheath syringepump 604 and sheath reservoir 651.

Both sample syringe pump 600 and bead syringe pump 602 independentlyrotate the syringe barrels using rotation stepper motors 625 and 626,respectively. These two syringe pumps and sheath syringe pump 606 usestepper motors 610, 611, 612 to drive the syringe plungers in order tocontrol the dispensing rate. Additionally, stepper motors 615, 616 and617 allow for the transmission of all three syringe pumps from low speedinjection/aspiration to high speed injection/aspiration. Thistransmission mechanism is explained in detail in the above referencedprovisional patent application.

Initialization of the Fluidic System with Bead Suspension Fluid andSheath Fluid

During the initialization phase of the system, assuming all syringes areloaded with their respective fluids, sheath valve 662 is actuated usingsheath valve stepper motor 622, to couple sheath syringe pump 604 to aninput sheath pressure tank 672, via a 50 micron diameter fluidic line634. Sheath pressure tank 672 serves to decrease the inherentpulsatility in sheath syringe pump 604 by having input fluidic line 634and output fluidic line 633 partially filled with sheath fluid so as toallow for air dampening of the pulsatility. Sheath pressure tank 672 hasa vent line 641, which is connected to a pressure relief valve 670, awaste tank line 640 and a waste tank 650 vented to atmospheric pressurevia a 0.2 micron air filter 681. The sheath fluid in output fluidic line633 enters a hydrodynamically focused flow cell 678, thereby achievingthe outer sheath fluid flow in the flow cell.

Bead valve 661 is actuated using bead valve stepper motor 621 to connectbead syringe pump 602 to the sample input of hydrodynamically focusedflow cell 678 via a 25 micron diameter bead injection fluidic line 630,and a 25 micron sample and bead union injection fluidic line 632. Thesolution of calibration beads enters hydrodynamically focused designedflow cell 678, thereby producing the core fluid in the flow cell.

Sample Analysis

Once the imaging system determines that the core/sheath fluid flow inthe hydrodynamically focused flow cell is stable, a sample fluidcontaining the objects of interest can be injected into the core streamof the flow cell. Sample valve 660 is actuated using sample valvestepper motor 620, to couple the sample input of hydrodynamicallyfocused flow cell 678 to bead injection fluidic line 630 and sample andbead union injection fluidic line 632. Note that up until this point thecalibration bead fluid from bead syringe pump 602 has been the been thesole component of the core fluid in the flow cell. Several options canbe employed to introduce a sample fluid into the core fluid at thispoint.

A first option is to introduce the sample fluid (using sample syringepump 600) at a given flow rate, and to reduce the flow rate of thecalibration bead solution (using bead syringe pump 602) so as topreserve the same overall flow rate of the core fluid flowing in sampleand bead union injection fluidic line 632. This option will preserve thestability of the core/sheath fluid flow in the flow cell.

A second option is to introduce the sample fluid (using sample syringepump 600) at a given flow rate without adjusting the calibration beadsolution flow rate, thereby increasing the overall core fluid flow rate.

A third option is to introduce the sample fluid (using sample syringepump 600) at a given flow rate, and to terminate the flowing of the beadsolution.

Post Analysis

Note that FIG. 3.0 depicts the core and sheath fluids exiting flow cell678 via a 50 micron diameter fluidic line 638, which is coupled to a 50micron diameter fluidic line 639 that flows into a waste tank 650. Avalve 663 could be used to direct the core and sheath fluids in fluidicline 638 to a destination (not shown) other than waste tank 650. Forexample, the core and sheath fluids from fluidic line 638 could berecirculated back into flow cell 678, or could be directed to a fluidinlet of an additional analytical device for further analysis.

Maintenance of Fluidic Pathways

Valves 663 and 664 preferably can be actuated by stepper motors 623 and624 to allow for back flushing of the fluidic lines, and the flow cell,to remove obstructions. Additionally, such valves can be used to purgefluidic lines of fluid, as well as to fill fluidic lines withcleaning/sterilization agents such as alcohol.

Alternative Embodiments

Single Syringe Pump for both Sample and Calibration Beads: Oneanticipated embodiment of a simplified fluidic system utilizes only twofluidic pumps, one for providing the sheath fluid and a second pump forthe combined delivery of sample fluid and calibration bead fluid. Insuch an embodiment, the sample and calibration beads are premixed andsimultaneously present in the combined second syringe pump.Additionally, it would be possible to operate such an analysisinstrument without the use of a sheath fluid, thus an even simplerembodiment could employ only a single fluid pump.

FIG. 54 shows the overall steps for employing calibration beads toenhance the performance of a flow imaging system. In a block 700specific calibration beads are selected, based on the parameters of theflow imaging system. As noted above, the size of calibration beads mustbe matched the imaging system that is being used, to ensure that thecalibration beads can pass through the fluidic system. It is alsodesirable to select calibration beads that are expected to provide aconsistent data signal, either based on empirical experience or based ontheoretical considerations. For certain imaging systems, desirablecalibration beads might include a label (such as a fluorescent dye) thatis readily detected by the flow imaging system. Including such a labelwould be particularly effective if it was known that the sample (theobjects of interest that will be imaged by the flow imaging system oncethe flow imaging system is stable) does not include the same label(s).

In an optional block 702, a desired concentration can be calculated. Asdiscussed above, based on the characteristics of the detector and theflow rate, it is possible to determine a concentration of calibrationbeads that is expected to provide a substantially continuous datasignal. While this step is preferred, it has been indicated as anoptional step, because it is anticipated that highly concentratedcalibration fluids (i.e. fluids containing in excess of 1×10⁸calibration beads per milliliter of fluid) will likely be sufficientlyconcentrated to provide a consistent data signal.

In a block 704 the calibration beads are introduced into flow imagingsystem, preferably employing one of the fluidic pump systems describedabove. Calibration bead fluid is provided on an ongoing basis at apredetermined flow rate (based on design parameters of the imagingsystem) until the flow imaging system stabilizes. While suchstabilization might simply mean that a desired sheath flow and core flowis established, an optional block 706 indicates that in at least someembodiments the establishment of TDI synchronization is desirable. Therelationship between the acquisition of reliable velocity data and theability of the flow imaging system to synchronize the TDI detector tothe fluid flow has been discussed in detail above.

In an optional block 708, selected diagnostic and calibration procedurescan be performed. Such procedures, discussed in greater detail above,include the determination of the point spread function, sensitivitycalibration, and optical system focus.

In a block 710, the sample fluid is introduced into the flow imagingsystem. As noted above, while the sample fluid is introduced into theflow cell of the flow imaging system (specifically to the core flow ofthe flow cell), the calibration fluid can be maintained at a constantflow rate, reduced proportional to the amount of sample fluidintroduced, or eliminated entirely.

In a block 712, the flow imaging system collects sample data based onthe images of the sample objects.

Although the present invention has been described in connection with thepreferred form of practicing it, those of ordinary skill in the art willunderstand that many modifications can be made thereto within the scopeof the claims that follow. Accordingly, it is not intended that thescope of the invention in any way be limited by the above description,but instead be determined entirely by reference to the claims thatfollow.

The invention in which an exclusive right is claimed is defined by thefollowing:
 1. A method for using an engineered sample of calibrationobjects to enhance the performance of a flow imaging system configuredto obtain images of sample objects entrained in a flow of fluid passingthrough the flow imaging system, using a time-delay-integration (TDI)detector, comprising the steps of: (a) providing an engineered sample ofcalibration objects that enables a reliable data signal to be obtainedduring each update cycle of the flow imaging system, each calibrationobject being sufficiently small so as to readily pass through thefluidics of the flow imaging system; (b) introducing the engineeredsample of calibration objects into the flow of fluid passing through theflow imaging system; (c) using the flow imaging system to collect datacorresponding to the engineered sample of calibration objects, the dataenabling the flow imaging system to reach and maintain a stable state,the data including velocity data that are used to synchronize the TDIdetector to the flow of fluid passing through the flow imaging system,the data further including diagnostic data indicating whether the flowimaging system is functioning as desired; and (d) introducing sampleobjects into the flow of fluid passing through the flow imaging system,whereby synchronization of the TDI detector to the flow of fluid passingthrough the flow imaging system that is facilitated by the engineeredsample of calibration objects enhances the ability of the flow imagingsystem to collect data corresponding to the sample objects.
 2. A methodfor using calibration objects to enhance the performance of a flowimaging system configured to obtain images of sample objects entrainedwithin a flow of fluid passing through the flow imaging system using atime-delay-integration (TDI) detector, comprising the steps of: (a)introducing a plurality of calibration objects entrained in a flow offluid into the flow imaging system; (b) modulating light from theplurality of calibration objects using an optical grating to producemodulated light having a modulation frequency that varies as a functionof the velocity of the flow of fluid; (c) using the modulated light todetermine the velocity of the fluid; (d) using the velocity tosynchronize the TDI detector to the flow of fluid passing through theflow imaging system; and (e) using data collected by the flow imagingsystem corresponding to the calibration objects to perform a diagnosticcheck on the flow imaging system.
 3. The method of claim 2, wherein thediagnostic check is performed under at least one of the followingconditions: (a) before sample objects are introduced in the flow offluid; (b) while sample objects are introduced in the flow of fluid; and(c) after sample objects have been introduced in the flow of fluid. 4.The method of claim 2, wherein: (a) at least some of the calibrationobjects are fluorescently labeled; and (b) the step of using datacollected by the flow imaging system corresponding to the calibrationobjects to perform a diagnostic check on the flow imaging systemcomprises the step of using spectral data collected from the calibrationobjects to calibrate the flow imaging system.
 5. The method of claim 2,wherein the step of using data collected by the flow imaging systemcorresponding to the calibration objects to perform a diagnostic checkon the flow imaging system comprises at least two of the followingsteps: (a) determining a point spread function associated with opticalcomponents of the flow imaging system; (b) determining if the flowimaging system is properly focused; (c) determining if an axis of theflow of fluid is properly aligned relative to the flow imaging system;and (d) determining if the TDI detector is synchronized to the flow offluid.
 6. The method of claim 2, wherein the step of introducing aplurality of calibration objects entrained in a flow of fluid into theflow imaging system comprises the step of introducing calibrationobjects that range from about 1 micron in diameter to about 10 micronsin diameter.
 7. The method of claim 2, wherein the step of introducing aplurality of calibration objects entrained in a flow of fluid into theflow imaging system comprises the step of introducing at least one ofthe following calibration object sets: (a) a first calibration objectset comprising: (i) a plurality of calibration objects of relativelysmaller size, the relatively smaller size having been selected toenhance the determination of the point spread function; and (ii) aplurality of calibration objects of relatively larger size, therelatively larger size having been selected to enhance the determinationof the velocity of the flow of fluid; and (b) a second calibrationobject set comprising: (i) a plurality of calibration objects of thathave been fluorescently labeled, the fluorescent labels having beenselected to facilitate calibration of the flow imaging system; and (ii)a plurality of calibration objects having a specified size, thespecified size having been selected to enhance the determination of thevelocity of the flow of fluid.
 8. A method for using calibration objectsto enhance the performance of a flow imaging system configured to obtainimages of sample objects entrained within a flow of fluid passingthrough the flow imaging system using a time-delay-integration (TDI)detector, comprising the steps of: (a) introducing a plurality ofcalibration objects entrained in a flow of fluid into the flow imagingsystem; (b) using data collected by the flow imaging systemcorresponding to the calibration objects to determine a velocity of theflow of fluid; (c) using the velocity to synchronize the TDI detector tothe flow of fluid; and (d) using data collected by the flow imagingsystem corresponding to the calibration objects to perform a diagnosticcheck on the flow imaging system.
 9. The method of claim 8, wherein thediagnostic check is performed before sample objects are introduced inthe flow of fluid.
 10. The method of claim 8, wherein the diagnosticcheck is performed while sample objects are introduced in the flow offluid.
 11. The method of claim 8, wherein the diagnostic check isperformed after sample objects have been introduced in the flow offluid.
 12. The method of claim 8, wherein the step of using datacollected by the flow imaging system corresponding to the calibrationobjects to perform a diagnostic check on the flow imaging systemcomprises the step of determining a point spread function associatedwith optical components of the flow imaging system.
 13. The method ofclaim 12, further comprising the step of adjusting the opticalcomponents of the flow imaging system to achieve a desired point spreadfunction.
 14. The method of claim 8, wherein: (a) at least some of thecalibration objects are fluorescently labeled; and (b) the step of usingdata collected by the flow imaging system corresponding to thecalibration objects to perform a diagnostic check on the flow imagingsystem comprises the step of using spectral data collected from thecalibration objects to calibrate the flow imaging system.
 15. The methodof claim 8, wherein the step of using data collected by the flow imagingsystem corresponding to the calibration objects to perform a diagnosticcheck on the flow imaging system comprises at least one of the followingsteps: (a) determining if the flow imaging system is properly focused;(b) determining whether the flow of fluid is aligned with the desiredaxis within the flow imaging system; and (c) determining if the TDIdetector is synchronized to the flow of fluid.
 16. The method of claim15, wherein the step determining whether the flow of fluid is alignedwith the desired axis within the flow imaging system comprises the stepof comparing the lateral positions of a plurality of images of differentcalibration objects obtained by the TDI detector.
 17. The method ofclaim 15, wherein the step determining whether the TDI detector issynchronized to the flow of fluid comprises the step of analyzing aplurality of images of different calibration objects obtained by the TDIdetector, elongated and shortened images being indicative ofinsufficient synchronization.
 18. The method of claim 8, wherein thestep of introducing a plurality of calibration objects entrained in aflow of fluid into the flow imaging system comprises the step ofintroducing calibration objects that range from about 1 micron indiameter to about 10 microns in diameter.
 19. The method of claim 8,wherein the step of introducing a plurality of calibration objectsentrained in a flow of fluid into the flow imaging system comprises thestep of introducing: (a) a plurality of calibration objects of that havebeen fluorescently labeled, the fluorescent labels having been selectedto facilitate calibration of the flow imaging system; and (b) aplurality of calibration objects having a specified size, the specifiedsize having been selected to enhance the determination of the velocityof the flow of fluid.
 20. The method of claim 8, wherein the step ofintroducing a plurality of calibration objects entrained in a flow offluid into the flow imaging system comprises the step of introducing:(a) a plurality of calibration objects of relatively smaller size, therelatively smaller size having been selected to enhance thedetermination of the point spread function; and (b) a plurality ofcalibration objects of relatively larger size, the relatively largersize having been selected to enhance the determination of the velocityof the flow of fluid.
 21. The method of claim 20, wherein the relativelysmaller size calibration objects have been selected as a function ofoptical characteristics of the flow imaging system and a pixel size ofthe TDI detector, such that images of individual calibration objects ofthe relatively smaller size collected by the TDI detector aresubstantially confined to a single pixel.
 22. The method of claim 20,wherein the relatively larger size calibration objects have beenselected to fall within a range of about 1 micron to about 10 microns indiameter, and the relatively smaller size calibration objects have beenselected to fall within a range of about 200 nanometers to about 500nanometers in diameter.
 23. The method of claim 8, wherein the step ofintroducing a plurality of calibration objects entrained in a flow offluid into the flow imaging system comprises the step of introducing atleast some calibration objects having a first size range that isselected as a function of optical characteristics of the flow imagingsystem and a pixel size of the TDI detector, such that images ofindividual calibration objects of the first size range collected by theTDI detector are substantially confined to a single pixel.
 24. Themethod of claim 8, wherein the step of introducing a plurality ofcalibration objects entrained in a flow of fluid into the flow imagingsystem comprises the steps of: (a) determining a flow rate ofcalibration objects required to ensure at least one calibration objectis imaged by the TDI detector per update cycle; and (b) substantiallyproviding the flow rate of calibration objects so determined.
 25. Themethod of claim 8, wherein: (a) the step of introducing a plurality ofcalibration objects entrained in a flow of fluid into the flow imagingsystem comprises the steps of: (i) establishing a flow of a sheath fluidthrough the flow imaging system; and (ii) establishing a flow of a corefluid through the flow imaging system, the calibration objects beingentrained within the core fluid; and (b) the step of the step of usingdata collected by the flow imaging system corresponding to thecalibration objects to perform a diagnostic check on the flow imagingsystem comprises the step determining whether the core fluid ishydrodynamically focused by the sheath fluid.
 26. The method of claim 8,wherein the step of introducing a plurality of calibration objectsentrained in a flow of fluid into the flow imaging system comprises thestep of selecting a desired size of the calibration objects as afunction of a size of sample objects to be analyzed, such that thedesired size of each calibration object is relatively larger than thesize of the sample objects, and as a consequence, while the sampleobjects are being analyzed by the flow imaging system, the relativelylarger size calibration objects enable the velocity of the flow of fluidto be determined, ensuring that the TDI detector remains synchronized tothe flow of fluid passing through the flow imaging system.
 27. Themethod of claim 8, further comprising the step of introducing aplurality of sample objects entrained in a flow of fluid into the flowimaging system, such that images of the sample objects incident on theTDI detector are enhanced due to the synchronization of the TDI detectorto the flow of fluid passing through the flow imaging system.
 28. Themethod of claim 27, wherein the step of introducing a plurality ofsample objects entrained in a flow of fluid into the flow imaging systemis performed after the step of introducing a plurality of calibrationobjects entrained in a flow of fluid into the flow imaging system, suchthat the plurality of calibration objects facilitate the synchronizationof the TDI detector to the flow of fluid before the sample objects areintroduced.
 29. The method of claim 28, wherein the introduction of theplurality of calibration objects is terminated when the plurality ofsample objects are introduced.
 30. The method of claim 28, wherein theplurality of sample objects are entrained in a sample fluid, and theplurality of calibration objects are entrained in a calibration fluid,and the step of introducing a plurality of sample objects entrained in aflow of fluid into the flow imaging system comprises the step ofmaintaining a total volumetric flow of fluid through the flow imagingsystem constant, such that any increase in volumetric fluid flowattributable to introduction of the sample fluid is offset by acorresponding reduction in a volumetric fluid flow of the calibrationfluid.
 31. The method of claim 27, wherein the step of introducing aplurality of sample objects entrained in a flow of fluid into the flowimaging system is performed concurrently with the introduction of theplurality of calibration objects.
 32. The method of claim 31, whereinthe plurality of sample objects and the plurality of calibration objectsare entrained in the same flow of fluid.
 33. A method for usingcalibration beads to enhance the performance of a flow imaging systemconfigured to obtain images of sample objects entrained within a flow offluid passing through the flow imaging system using atime-delay-integration (TDI) detector, comprising the steps of: (a)establishing a flow of sheath fluid within the flow imaging system; (b)establishing a flow of core fluid within the flow imaging system, theflow of core fluid including a plurality of calibration beads, said flowof core fluid being surrounded by the flow of sheath fluid; (c) usingdata collected by the flow imaging system corresponding to thecalibration beads to determine a velocity of the flow of core fluid; (d)using the velocity to synchronize the TDI detector to the flow of corefluid within the flow imaging system; and (e) using data collected bythe flow imaging system corresponding to the calibration beads toperform a diagnostic check on the flow imaging system.
 34. The method ofclaim 33, wherein the diagnostic check is performed under at least oneof the following conditions: (a) before sample objects are introduced inthe flow of core fluid; (b) while sample objects are introduced in theflow of core fluid; and (c) after sample objects have been introduced inthe flow of core fluid.
 35. The method of claim 33, wherein the step ofusing data collected by the flow imaging system corresponding to thecalibration beads to perform a diagnostic check on the flow imagingsystem comprises at least two of the following steps: (a) determining apoint spread function associated with optical components of the flowimaging system; (b) determining if the flow imaging system is properlyfocused; (c) determining if an axis of the flow of core fluid isproperly aligned relative to the flow imaging system; (d) determining ifthe TDI detector is synchronized to the flow of fluid; and (e) when atleast some of the calibration beads are fluorescently labeled, usingspectral data collected from the calibration beads to calibrate the flowimaging system.
 36. A flow imaging system adapted to determine anindication of a velocity and at least one additional characteristic of aplurality of objects, from images of the plurality of objects, whilethere is relative movement between the plurality of objects and the flowimaging system, comprising: (a) a fluidic subsystem configured toselectively introduce a fluid in which the plurality of objects areentrained into a field of view, the plurality of objects including atleast one of a plurality of calibration objects and a plurality ofsample objects; (b) an optical subsystem configured to convey light fromthe plurality of objects passing through the field of view; (c) avelocity measurement subsystem configured to receive the light conveyedby the optical subsystem, and to manipulate said light to determine anindication of the velocity of the relative movement between theplurality of objects and the flow imaging system; (d) atime-delay-integration (TDI) detector configured to receive the lightobtained by the optical subsystem, said TDI detector being coupled tosaid velocity measurement subsystem, said TDI detector employing atiming signal produced by the velocity measurement subsystem to producean output signal that is indicative of said at least one additionalcharacteristic of the plurality of objects; and (e) a control circuitlogically coupled with the TDI detector, the control circuit configuredto implement a plurality of functions, including using data collected bythe flow imaging system corresponding to the plurality of calibrationobjects to perform at least one diagnostic check on the flow imagingsystem.
 37. The flow imaging system of claim 36, wherein said controlcircuit is further configured to implement the function of performingthe at least one diagnostic check under at least one of the followingconditions: (a) before sample objects are introduced in the flow offluid; (b) while sample objects are introduced in the flow of fluid; and(c) after sample objects have been introduced in the flow of fluid. 38.The flow imaging system of claim 36, wherein the at least one diagnosticcheck the control circuit is configured to implement comprises at leasttwo of the following diagnostic checks: (a) determining a point spreadfunction associated with optical components of the flow imaging system;(b) determining if the flow imaging system is properly focused; (c)determining if an axis of the flow of fluid is properly aligned relativeto the flow imaging system; (d) determining if the TDI detector issynchronized to the flow of fluid; and (e) when at least some of theplurality of calibration objects are fluorescently labeled, usingspectral data collected from the plurality of calibration objects tocalibrate the flow imaging system.
 39. The flow imaging system of claim36, wherein said fluidic subsystem comprises a syringe pump coupled witha fluid supply including a fluid into which a plurality of sampleobjects and a plurality of calibration objects are entrained.
 40. Theflow imaging system of claim 36, wherein said fluidic subsystemcomprises: (a) a first syringe pump coupled to a sample fluid supplyvolume configured to provide a supply of a sample fluid into which aplurality of the sample objects are entrained; and (b) a second syringepump coupled a calibration fluid supply volume configured to provide asupply of a calibration fluid into which a plurality of the calibrationobjects are entrained.
 41. The flow imaging system of claim 40, whereinsaid control circuit is controllably coupled to the fluidic subsystemand is further configured to implement the functions of: (a) activatingthe fluidic subsystem so that a flow of calibration fluid is establishedwithin the field of view, thus enabling the velocity measurementsubsystem to provide the timing signal to the TN detector; and (b)activating the fluidic subsystem so that a flow of sample fluid isestablished within the field of view if the diagnostic check indicatesthat the flow imaging system is ready to analyze the sample objects. 42.The flow imaging system of claim 40, wherein: (a) the fluidic subsystemfurther comprises a sheath fluid supply volume configured to provide asupply of a sheath fluid; (b) the control circuit is further configuredto activate the fluidic subsystem so that a flow of sheath fluid isestablished within the field of view before the calibration fluid isintroduced into the field of view; and (c) the diagnostic checkimplemented by the control circuit compares a plurality of images ofcalibration objects incident upon the TDI detector to determine if therelative lateral positions of the plurality of images of the calibrationobjects indicate that the flow of calibration fluid in the field of viewis properly hydrodynamically focused by the sheath fluid.
 43. The flowimaging system of claim 40, wherein the control circuit maintains theflow of fluid through the field of view at a constant volumetric flowrate, such that any increase in the volumetric flow rate attributable tointroduction of the sample fluid into the field of view is offset by acorresponding reduction in a volumetric flow rate of the calibrationfluid into the field of view.
 44. The flow imaging system of claim 36,wherein said velocity measurement subsystem comprises: (a) an opticalgrating disposed to receive the light conveyed by the optical subsystem,said optical grating modulating the light, producing modulated lightthat has a modulation frequency corresponding to a velocity of therelative movement between the plurality of objects and the flow imagingsystem; (b) a light sensitive detector disposed to receive the modulatedlight, said light sensitive detector producing an electrical signal inresponse to the modulated light; and (c) means coupled to the lightsensitive detector to receive the electrical signal, for determining anindication of the velocity of the relative movement between the objectand the flow imaging system as a function of the electrical signal andfor producing a timing signal as a function of said indication of thevelocity.
 45. The flow imaging system of claim 36, wherein the controlcircuit comprises at least one of: (a) processor coupled to a memory,said memory storing machine instructions that are executed by theprocessor to implement the plurality of functions; and (b) anapplication specific integrated circuit.
 46. A flow imaging systemadapted to determine an indication of a velocity and at least oneadditional characteristic of a plurality of objects, from images of theplurality of objects, while there is relative movement between theplurality of objects and the flow imaging system, comprising: (a) afluidic subsystem configured to selectively introduce a fluid into whichthe plurality of objects are entrained into a field of view, theplurality of objects including at least one of calibration objects andsample objects; (b) an optical subsystem configured to convey light fromthe plurality of objects passing through the field of view; (c) avelocity measurement subsystem configured to receive the light conveyedby the optical subsystem and to produce a signal indicative of thevelocity of the relative movement between the plurality of objects andthe flow imaging system; (d) a time-delay-integration (TDI) detectorconfigured to receive the light conveyed by the optical subsystem, saidTDI detector producing an output signal that is indicative of said atleast one additional characteristic of the object; and (e) a controlcircuit logically coupled with the velocity measurement subsystem andthe TDI detector, the control circuit implementing a plurality offunctions, including: (i) using the signal indicative of the velocity tosynchronize the TDI detector to the flow of fluid passing through theflow imaging system; and (ii) using the output signal to perform atleast one diagnostic check on the flow imaging system.
 47. The flowimaging system of claim 46, wherein said control circuit is furtherconfigured to use the output signal to perform at least one diagnosticcheck on the flow imaging system under at least one of the followingconditions: (a) before sample objects are introduced in the flow offluid; (b) while sample objects are introduced in the flow of fluid; and(c) after sample objects have been introduced in the flow of fluid. 48.The flow imaging system of claim 46, wherein the at least one diagnosticcheck the control circuit is configured to implement comprises at leasttwo of the following diagnostic checks: (a) determining a point spreadfunction associated with optical components of the flow imaging system;(b) determining if the flow imaging system is properly focused; (c)determining if an axis of the flow of fluid is properly aligned relativeto the flow imaging system; (d) determining if the TDI detector issynchronized to the flow of fluid; and (e) when at least some of thecalibration beads are fluorescently labeled, using spectral datacollected from the calibration beads to calibrate the flow imagingsystem.
 49. A flow imaging system adapted to determine an indication ofa velocity and at least one additional characteristic of an object, fromimages of the object, while there is relative movement between theobject and the imaging system, comprising: (a) a fluidic subsystemconfigured to selectively introduce a fluid into which at least one typeof object is entrained into a field of view, the fluid systemcomprising: (i) a sample fluid supply volume configured to provide asupply of a sample fluid in which a plurality of sample objects areentrained; (ii) a calibration fluid supply volume configured to providea supply of a calibration fluid in which a plurality of calibrationobjects are entrained; (b) an optical subsystem configured to conveylight from an object passing through the field of view; (c) a velocitymeasurement subsystem configured to receive the light conveyed by theoptical subsystem, and to manipulate said light to determine theindication of the velocity of the relative movement between the objectand the flow imaging system; (d) a time-delay-integration (TDI) detectorconfigured to receive the light conveyed by the optical subsystem, saidTDI detector producing an output signal that is indicative of said atleast one additional characteristic of the object; and (e) a controlcircuit controllably coupled with the fluidic subsystem and logicallycoupled with the TDI detector, the control circuit implementing aplurality of functions, including: (i) activating the fluidic subsystemso that a flow of the calibration fluid is established within the fieldof view, thus enabling the velocity measurement subsystem tocontinuously determine the indication of velocity; (ii) using datacollected by the flow imaging system corresponding to the calibrationbeads to perform at least one diagnostic check on the flow imagingsystem; and (iii) activating the fluidic subsystem so that a flow ofsample fluid is established within the field of view, such that imagesof the sample objects incident on the TDI detector are enhanced due to asynchronization of the TDI detector to the flow of fluid passing throughthe flow imaging system enabled by the indication of velocity.
 50. Theflow imaging system of claim 49, wherein the control circuit islogically coupled with the velocity measurement subsystem, and thecontrol circuit is further configured to implement the function of usingthe indication of velocity determined by the velocity measurementsubsystem to continuously synchronize the TDI detector to the flow offluid passing through the flow imaging system.
 51. The flow imagingsystem of claim 49, wherein the TDI detector is logically coupled to thevelocity measurement subsystem, and is configured to use the indicationof velocity determined by the velocity measurement subsystem tocontinuously synchronize the TDI detector to the flow of fluid passingthrough the flow imaging system.
 52. The flow imaging system of claim49, wherein the control circuit is further configured to activate thefluidic subsystem so that a flow of sample fluid is established withinthe field of view only after the at least one diagnostic check on theflow imaging system indicates that the flow imaging system is ready toanalyze sample objects.
 53. The flow imaging system of claim 49, whereinsaid control circuit is further configured to perform the at least onediagnostic check on the flow imaging system under at least one of thefollowing conditions: (a) before sample objects are introduced in theflow of fluid; (b) while sample objects are introduced in the flow offluid; and (c) after sample objects have been introduced in the flow offluid.
 54. The flow imaging system of claim 49, wherein the at least ondiagnostic check implemented by the control circuit comprises at leastone of: (a) determining a point spread function associated with opticalcomponents of the flow imaging system; (b) determining if the flowimaging system is properly focused; (c) determining if an axis of theflow of calibration fluid is properly aligned relative to the flowimaging system; (d) determining the TDI detector is synchronized to theflow of calibration fluid; and (e) when at least some of the calibrationbeads are fluorescently labeled, using spectral data collected from thecalibration beads to calibrate the flow imaging system.
 55. The flowimaging system of claim 49, wherein the control circuit is furtherconfigured to implement the function of maintaining the flow of fluidthrough the field of view at a substantially constant volumetric flowrate, such that any increase in the volumetric flow rate attributable tothe introduction of the sample fluid into the field of view is offset bya corresponding reduction in a volumetric flow rate of the calibrationfluid into the field of view.
 56. The method for using calibrationobjects to enhance the performance of a flow imaging system configuredto obtain images of sample objects entrained within a flow of fluidpassing through the flow imaging system using a time-delay-integration(TDI) detector, comprising the steps of: (a) introducing a plurality ofcalibration objects entrained in a flow of fluid into the flow imagingsystem, such that the calibrations objects are larger in size thanobjects of interest to be imaged by the flow imaging system; (b)modulating light from the plurality of calibration objects using anoptical grating to produce modulated light having a modulation frequencythat varies as a function of the velocity of the flow of fluid; (c)using the modulated light to determine the velocity of the fluid; (d)using the velocity to synchronize the TDI detector to the flow of fluidpassing through the flow imaging system; and (e) using data collected bythe flow imaging system corresponding to the calibration objects toperform a diagnostic check on the flow imaging system, such that a sizedifference between the calibrations objects and the objects of interestenable data corresponding to the calibration objects to be easilydiscriminated from data corresponding to an object of interest.
 57. Amethod for using calibration objects to enhance the performance of aflow imaging system configured to obtain images of sample objectsentrained within a flow of fluid passing through the flow imaging systemusing a time-delay-integration (TDI) detector, comprising the steps of:(a) introducing a plurality of calibration objects entrained in a flowof fluid into the flow imaging system, such that the calibrationsobjects are larger in size than objects of interest to be imaged by theflow imaging system; (b) introducing a plurality of objects of interestentrained in a flow of a fluid into the flow imaging system; (c)modulating light from the plurality of calibration objects using anoptical grating to produce modulated light having a modulation frequencythat varies as a function of a velocity of the fluid; (d) using themodulated light to determine the velocity of the fluid; (e) using thevelocity to synchronize the TDI detector to the flow of fluid passingthrough the flow imaging system; (f) using the flow imaging system tocollect data corresponding to the plurality of calibration objects andthe plurality of objects of interest; and (g) separating data collectedby the flow imaging system corresponding to the plurality of calibrationobjects from data collected by the flow imaging system corresponding tothe plurality of objects of interest, based on a size difference betweenthe calibration objects and the objects of interest.
 58. The method ofclaim 57, further comprising the step of using data collected by theflow imaging system corresponding to the calibration objects to performa diagnostic check on the flow imaging system.
 59. The method of claim57, wherein the step of using data collected by the flow imaging systemcorresponding to the calibration objects to perform a diagnostic checkon the flow imaging system comprises at least one of the followingsteps: (a) determining a point spread function associated with opticalcomponents of the flow imaging system; (b) determining if the flowimaging system is properly focused; (c) determining if an axis of theflow of fluid is properly aligned relative to the flow imaging system;and (d) determining if the TDI detector is synchronized to the flow offluid.